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Integrated Measure of PRogram Element SuStainability in Childcare Settings (IMPRESS-C): development and psychometric evaluation of a measure of sustainability determinants in the early childhood education and care setting



There is a need for valid and reliable measures of determinants of sustainability of public health interventions in early childhood education and care (ECEC) settings. This study aimed to develop and evaluate the psychometric and pragmatic properties of such a measure – the Integrated Measure of PRogram Element SuStainability in Childcare Settings (IMPRESS-C).


We undertook a two-phase process guided by the COnsensus-based Standards for the selection of health status Measurement INstruments checklist (COSMIN) and Psychometric and Pragmatic Evidence Rating Scale (PAPERS). Phase 1 involved measure development; i.e., determining items and scales through an iterative process and assessment of face and content validity. Phase 2 involved the evaluation of psychometric and pragmatic properties. The 29-item measure completed by service executives (directors and nominated supervisors) was embedded in a larger survey from a national sample of Australian ECEC services assessing their implementation of nutrition and physical activity programs. Structural validity, concurrent validity, known groups validity, internal consistency, floor and ceiling effects, norms, and pragmatic qualities of the measure were assessed according to the PAPERS criteria.


The final measure contained 26 items, with respondents reporting how strongly they agreed or disagreed on a five-point Likert scale. Phase 1 assessments confirmed the relevance, and face and content validity of the scale. In Phase 2, we obtained 482 completed surveys, of which 84% (n = 405) completed the entire measure across 405 ECEC settings (one executive per service). Three of the four fit indices for the confirmatory factor analysis met the pre-specified criteria (SRMR = 0.056, CFI = 0.993, RMSEA = 0.067) indicating ‘good’ structural validity. The IMPRESS-C illustrated: ‘good’ internal consistency, with Cronbach’s alpha values from 0.53 to 0.92; ‘emerging’ concurrent validity; ‘poor’ known groups validity; ‘good’ norms; and ‘good’ overall pragmatic qualities (cost, readability, length, and assessor burden).


The IMPRESS-C possesses strong psychometric and pragmatic qualities for assessing service executive-level perceptions of determinants influencing sustainment of public health interventions within ECEC settings. To achieve a full range of perspectives in this setting, future work should be directed to also develop and test measures of sustainability determinants at the implementer level (e.g., among individual educators and staff).

Peer Review reports


There are a multitude of effective evidence-based interventions (EBI) that are delivered in community settings to reduce risk factors for chronic disease and improve population health [1,2,3,4,5]. However, implementation of these EBIs, and their effects, often attenuate once initial implementation support or external funding is withdrawn [6, 7]. This has found to be the case for a range of interventions across community, education and clinical settings [6,7,8,9,10]. The sustained implementation of EBIs is important to ensure that they continue to yield positive effects among patients and populations, and that the considerable investment required to achieve successful initial implementation is not wasted [9].

Sustainability has been defined as ‘after a defined period of time, the program, clinical intervention, and/or implementation strategies continue to be delivered and/or individual behaviour change (i.e., clinician, patient) is maintained; the program and individual behaviour change may evolve or adapt while continuing to produce benefits for individuals/systems’ [11]. An important step in understanding and addressing EBI sustainability is the accurate identification and assessment of the characteristics, or determinants, that impact sustainability [10, 12, 13]. This enables researchers, policymakers and practitioners to develop strategies that address priority determinants to support EBI sustainability. Systematic reviews investigating the multi-level determinants of EBI sustainability have identified a number of factors perceived by stakeholders to be influential in the context of early educational settings [7, 14, 15]. The determinants most frequently identified in these settings include: the availability of equipment, resources and facilities, continued executive or leadership support, staff turnover, alignment with existing external policies, and workplace socio-cultural factors [7, 14, 15].

There are also a number of theories and frameworks that propose how these determinants interact and function to shape sustainability [9, 16,17,18]. One such framework, the Integrated Sustainability Framework by Shelton and colleagues, was informed by empirical evidence and comprehensively identifies and theorises the interactions between determinants found to be influential to sustainment across a range of interventions delivered in “real world” clinical and community settings [9]. Influential determinants are organised into five domains including Outer Contextual Factors, Inner Contextual Factors, Processes, Intervention Characteristics, and Implementer/Population Characteristics [9]. This framework provides a useful structure for understanding, assessing and addressing the determinants of program sustainability. Although there are validated measures available that cover aspects of these framework domains and constructs [19], there are currently no formal validated quantitative measures that align with, and comprehensively cover this framework, hindering the framework’s utility to better understand the determinants and mechanisms of EBI sustainability.

Improving measurement of key implementation constructs and their determinants, including those pertaining to sustainability, is a priority for the field [20]. These are often assessed using self-report measures completed by key informants within specific settings (i.e., executive leadership and practitioners involved in EBI delivery). To identify the accuracy and validity of self-report measures, it is important to undertake thorough psychometric evaluations. Specifically, such measures should comprehensively cover the intended construct [21], assess reliability [22], as well as important pragmatic qualities, including the measure’s ease of access, use, scoring, and interpretation [23, 24]. To minimise misunderstanding and increase measurement accuracy, it is also important to ensure the correct determinants are measured from relevant perspectives (i.e., specific questions asked for different roles – executive vs. implementer level) [20, 25]. For example, determinants relating to higher-level organisational structures and processes that occur (e.g., funding allocation or external partnership support) should be answered by executives within the organisation (i.e., Directors, Managers, Supervisors, Leaders) who have in-depth knowledge of such structures and processes [25].

High-quality systematic reviews have been conducted examining available measures of sustainability (as an outcome) and sustainability determinants across a range of settings, their psychometric and pragmatic properties, and how they have been empirically used [20, 26, 27]. The most recent of these conducted by Hall and colleagues in 2022 [20], provided researchers with a comprehensive guide to identify where robust and suitable measures exist and provide practical guidance to end-users in selecting the most relevant measure for their setting [20]. The review included 223 articles representing 28 individual measures, of which two assessed sustainability as an outcome [28, 29], 25 assessed sustainability determinants, and only one explicitly assessed both [30]. The review used the Psychometric and Pragmatic Evidence Rating Scale (PAPERS) to assess the psychometric and pragmatic qualities of each measure [24, 31]. The Provider Report of Sustainment Scale (PRESS) measure [28] was found to be the most psychometrically robust and pragmatic measure of sustainability, however this measure assesses sustainability as an outcome (i.e., continued delivery of an EBI), and does not cover important determinants found to influence EBI delivery. The highest rating measure of sustainability determinants was the School-wide Universal Behaviour Sustainability Index-School Teams (SUBSIST) [32], however this is limited to evaluating a specific EBI – School-wide Positive Behavioral Interventions and Supports within schools, and is not appropriate when considering other EBIs in other settings. Further, whilst the Clinical Sustainability Assessment Tool (CSAT) [33] and Sustainment Measurement System Scale (SMSS) [30] illustrated favourable psychometric and pragmatic qualities compared to other measures of sustainability determinants, it was recommended that the CSAT be considered for use when assessing sustainability determinants in clinical settings, and the SMSS for evaluating prevention programs and initiatives that have been or are currently funded by Substance Abuse and Mental Health Services Administration.

Evidently, whilst a range of measures have been identified, most have only been used once or a small number of times [28, 30, 34,35,36], are limited to a specific EBI [32, 34, 37,38,39], or have variable psychometric and pragmatic quality [29, 40,41,42], illustrating limited standardisation and quality in measurement [20, 27]. Furthermore, no measure of sustainability determinants has been developed and psychometrically evaluated within some key settings for the implementation of interventions focusing on children, such as early childhood education and care (ECEC) settings (i.e., formal, paid or government‐subsidised services that offer care for children six years and under, prior to commencing compulsory schooling [5]). The ECEC setting is a key target setting for implementing and sustaining effective chronic disease prevention interventions as they provide access to a large proportion of children for prolonged periods at critical stages in their development [43]. While there have been numerous EBIs in the ECEC setting found to be effective in improving child physical activity and healthy eating [4, 5], little is known about the determinants that impact their sustainability, with only two previous studies actively investigating sustainability determinants in the ECEC setting [15, 45].

As the organisational structure, curriculum, staffing, type of interventions and delivery processes differ considerably across settings [44], including ECEC, so too are the factors likely contributing to EBI sustainability [15, 45]. This presents a growing need to investigate these factors to help support intervention sustainability in the ECEC setting. However, systematic review evidence illustrates that in comparison to other settings, such as schools, there are no known validated measures of sustainability determinants available in this setting [20]. Therefore, the development and consistent use of large-scale, setting-specific, psychometrically robust, and pragmatic measures of sustainability determinants in ECEC services is required, to improve our understanding of what influences EBI sustainability in this setting. Therefore this study aimed to:

  1. 1.

    Develop a self-report measure – Integrated Measure of PRogram Element SuStainability in Childcare Settings (IMPRESS-C) designed to assess determinants of sustainability of evidence-based public health interventions in ECEC settings from the perspective of the service executive.

  2. 2.

    Evaluate psychometric properties of the measure, including: structural validity; concurrent validity; known groups validity; internal consistency; floor and ceiling effects; and norms.

  3. 3.

    Assess pragmatic properties of the measure, including: cost; readability; training; length; ease of access; and interpretation.


The processes for development and psychometric evaluation of the IMPRESS-C were guided by the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist [46], and Psychometric and Pragmatic Evidence Rating Scale (PAPERS) [24, 31]. These are regarded as gold standard guidelines for measure development [46], and assessment of measure psychometric and pragmatic properties [24, 31]. As recommended, the development of this measure was conducted over two phases: Phase 1: item development, face and content validity; and Phase 2: psychometric and pragmatic evaluation.

Phase 1: item development, face and content validity

Item development

Measure domains and items were informed by constructs from the Integrated Sustainability Framework [9] and systematic review evidence of determinants of sustainability in the ECEC setting [15, 45]. The Integrated Sustainability Framework was selected as it: (i) highlights key multi-level determinants that the emerging evidence suggests are important for facilitating intervention sustainability across a range of types of settings, including ECEC services [9]; (ii) can help identify and organise determinants that may be important in facilitating sustainability of an EBI; and (iii) provides clear definitions for how determinants can be categorised into framework domains [15]. The framework organises sustainability determinants into the following five domains: Outer Contextual Factors, Inner Contextual Factors, Processes, Characteristics of the Interventionist and Population, and Characteristics of the Intervention [9] (see Table 1).

Table 1 Integrated Sustainability Framework domains with corresponding factors covered, definitions, and examples of application

First, clear definitions for each domain deemed important to the ECEC setting were developed. These definitions were informed based on the framework, reviewed and iteratively updated by an expert panel, including one of the developers of the framework and experts in the ECEC setting, as well as experts in sustainability, measure development and psychometric evaluation. Second, an initial item pool of 87 items across the five framework domains was deductively generated [21] based on literature review evidence [15] and insight of eight content experts across the fields of implementation science, psychometric scale development, and education. Third, items were reduced through iterative discourse between the research team and the same content experts until consensus was reached on a comprehensive list of items (adequately covering all framework domains) without duplicates. Upon completion of this phase, the measure consisted of 42 items across five sustainability domain subscales: Outer Contextual Factors (5 items), Inner Contextual Factors (10 items), Processes (9 items), Characteristics of the Interventionist and Population (6 items), and Characteristics of the Intervention (12 items). The measure utilised a five-point Likert scale for each item, with response options: strongly agree; agree; neither agree nor disagree; disagree; strongly disagree. This was informed by other response scales of similar measures [47, 48] and recommendations of content experts in psychometric scale development.

Assessment of content validity and face validity

Content validity is the extent to which the items represent the constructs that a tool is designed to measure [21, 49]. Face validity is a component of content validity, and relates to the degree to which end-users deem the items as an appropriate representation of the target constructs [49]. An advisory group consisting of five content experts including two implementation scientists, two service delivery staff, and a Nominated Supervisor in an ECEC service, who were independent from those directly involved in generation of the initial item pool reviewed the initial measure. The advisory group reviewed the content and face validity of the measure by considering the following: (i) “are the items of the measure relevant to what’s being measured?”; (ii) “does the measurement method seem useful for measuring the variable/construct?”; and (iii) “is the measure seemingly appropriate for capturing the variable/construct?” The advisory group also reviewed each item to minimise misunderstanding and subsequent measurement error by enhancing item clarity, comprehensibility and relevance to the target population (ECEC service executives) [21]. Following Phase 1, the number of scale items reduced to 29.

Phase 2: psychometric and pragmatic evaluation

Phase 2 involved a formal evaluation to assess the psychometric properties and pragmatic features of the IMPRESS-C according to the PAPERS criteria, which uses a six-point Likert scale ranging from − 1 (poor) to 4 (excellent) [24, 31]. The methods used are described below.

Ethical approval

We sought approval for this study from the Hunter New England Human Research Ethics Committee (no. 06/07/26/4.04 2019/ETH12353) and University of Newcastle Human Research Ethics Committee (no. H-2008–0343).

Design and sample

The psychometric and pragmatic properties of the scale were assessed via a national cross-sectional survey with executive staff (i.e., Nominated Supervisors, Service Directors, Service Owners and Room Leaders) from ECEC services across all six states and two territories of Australia. The preferred respondent from each service was the Nominated Supervisor. A sample size of 2,000 ECEC services was chosen to account for a 50% consent rate for the larger survey. The national sample was extracted from the publicly available Australian Children's Education & Care Quality Authority (ACECQA) register. All ECEC services in Australia are required to be listed in this register as part of national accreditation processes. To obtain a sample of 2,000 services, 2,050 services (oversampling to account for ineligible services) were randomly selected, and stratified by state, using a random number generator in Microsoft Excel.


Services were eligible if they were a centre-based ECEC service (preschools and long day cares) approved by ACECQA. Long day care services provide centre-based care for children from 6 weeks to under 6 years of age for eight or more hours per day. Preschools typically enrol children between 3 and 6 years of age and provide care for 6 to 8 h per day [50].

Services were ineligible if they were:

  • A family day care service or provided only outside of school hours (OOSH) care;

  • A Department of Education service (i.e., attached to a school due to falling under a different ethics jurisdiction);

  • Temporarily closed according to ACECQA and based on telephone calls made from the research team;

  • Operating only on Saturday and/or Sunday; or

  • Providing care solely for children with special needs


Services were recruited using a staggered approach from August 2021 to April 2022. Each week approximately 250 services received an email inviting them to participate in the survey either online or via computer-assisted telephone interview (CATI). The order of contact was not randomised. A link directed services to the information statement (available for download), which then led to the online survey. Services were also mailed a hardcopy of the information statement, informing them that they will receive an email and a phone call inviting them to complete the survey. Approximately one week after the initial invitation, services which had not yet completed the online survey (including partial-completion) were first sent a reminder email and then a phone call by trained interviewers, inviting them to complete the survey via CATI.

Data collection

Surveys consisted of the 29-item IMPRESS-C, where respondents reported on the extent to which a number of factors influenced the continued delivery of an evidence-based program (targeting healthy eating or physical activity) at their service (a complete list of programs is illustrated in Additional file 1). Respondents were asked to complete the measure for one specific health promotion program. These programs were selected on the basis of systematic review findings [4, 5] and recommended for the setting to improve child healthy eating or physical activity. Each program of interest was selected based on previous responses of what programs were being implemented by the service. If the service was implementing multiple relevant programs, the program was assigned based on a hierarchy of programs. The hierarchy was initially determined based on the likelihood of services to implement each program across jurisdictions and was regularly updated throughout data collection to ensure an even distribution of responses to each of the included programs. The survey also asked for respondents’ demographics (current position, employment status, and highest level of relevant qualification completed that is related to ECEC employment); service characteristics (service type i.e., long day care or preschool, service hours of operation, age groups service cares for, number of full-time, part-time and casual educators working at the service, and number of children that attend the service on an average day); and service-level implementation of physical activity and healthy eating programs.

Statistical analysis

Statistical analyses were undertaken in R version 4.0.2 [51, 52]. An overview of the specific psychometric properties and the statistical analyses used are described below.

Item investigation

Item responses and response patterns were initially assessed to identify any items that were poorly responded to, and reviewed for possible exclusion. This included the distribution of responses for each item and percentage of respondents missing each item (missing included “don’t know” and “prefer not to say” responses). Items with more than 10% missing data or with more than 90% of responses occurring on only one of the response options were considered for potential exclusion. Polychoric correlations between all pairs of items were reviewed to help identify any possible redundancies in the items, with those with a correlation coefficient above 0.8 reviewed for possible exclusion by the research team [53].

Structural validity

As the dimensionality of the measure was based on an existing framework and we had a clear hypothesis of how the items of the scale should relate to one another [54], a confirmatory factor analysis (CFA) proposing a four-factor structure was selected. Diagonally weighted least squares was used as the estimation method due to the ordinal nature of the items [55, 56]. Parameter estimates were standardized with variances fixed at one. Missing responses, including those who answered ‘refused’ or ‘don’t know’ were imputed using a single imputation with predictive mean matching [57]. Respondents who missed all measure items were excluded from the analysis. An initial model assuming no correlation between factors was estimated and then revised to allow for such correlations, as it was reasonable to assume a relationship existed between the theoretical constructs. The following fit statistics and recommended criteria were used to assess the overall adequacy of the model:

  • Standardized Root Square Residual (SRMR) < 0.08 [58, 59];

  • Comparative Fit Index (CFI) > 0.95 [60];

  • Root Mean Square Error of Approximation (RMSEA) < 0.07 [53, 58];

  • Model Chi-squared p-value > 0.05 [61].

To reduce selection bias we pre-specified the criteria used to determine adequate fit indices, selecting those that have been recommended as they have been found to be most insensitive to the sample size, model misspecification and parameter estimates used [61]. Modification indices and factor loadings were examined and used to revise the CFA model to ensure the most parsimonious, adequate fitting and theoretically justifiable model was selected. Specifically, items with low factor loadings (< 0.40) or cross-loadings were examined, in consultation with the research team for removal or model amendments. Standardized factor loadings and their associated standard error, and p-values were reported.

Floor and ceiling effects

The percentage of respondents reporting the lowest and highest possible score for each domain were calculated. Domains where > 15% of respondents obtain the lowest (floor) or highest (ceiling) score were considered indicative of floor and ceiling effects [49].


Descriptive statistics for the final domains were calculated, including: median, quartiles one and three, minimum and maximum, mean, and standard deviation.

Internal consistency

Cronbach’s alpha was calculated for each domain, with values between 0.70 and 0.95 considered acceptable [49].

Concurrent validity

The correlation was used to examine the association between mean domain scores of the measure and the number of years a service reported to be delivering their specific health promotion program. Due to the non-linear relationship between the two measures, the Spearman correlation (Rho) was used. It was hypothesised that a moderate to high positive relationship between the measure domain scores and months of program delivery would be found. This was an assessment of concurrent validity as it assessed the agreement or correlation between two measures that theoretically should be tapping into similar constructs, administered at the same time [62, 63]. If these are true determinants of sustainability then they should be moderately or highly related to length of program delivery as this is a key indicator of sustainability. Based on the PAPERS scale, a correlation coefficient between 0.10 and 0.29 was considered ‘emerging’, 0.30 and 0.49 considered ‘adequate’, 0.50 and 0.69 ‘good’, and > 0.70 ‘excellent’ [24].

Known groups validity

Associations between the mean measure domain scores with characteristics hypothesised to differ were assessed using regression analysis. The known groups to compare included type of program (i.e., targeting physical activity or healthy eating), and number of full-time staff. Specifically, we hypothesised there would be a statistically significant difference between: i) program type for all four domains of the IMPRESS-C; and ii) number of full-time staff for all four domains of the measure. These characteristics have been found to have implications for sustainability as different programs may require varying levels of resources, support, and infrastructure to be maintained over time [64]; and a higher number of full-time staff can potentially lead to increased program capacity, more personalised attention to respondents, and greater adherence to program guidelines [43]. The mean domain scores were modelled individually as fixed effects. For program, a binomial distribution with a logistic link was used. The odds ratio (OR) or count ratio (CR) with corresponding 95% CI and p-values were presented. For full-time staff, a negative binomial distribution with a logistic link was used. The mean domain scores were modelled individually as fixed effects.


Phase 1: item development, face and content validity

Of the initial 87-items, 58 were removed during item reduction processes. A total of 45 items were removed following expert feedback due to perceived duplication in item phrasing (17 items), or inadequacy of the item to cover the domain of interest (28 items). A further 13 items were removed following advisory group feedback due to difficulties in item interpretation (8 items) and limited perceived relevance or appropriateness for the ECEC setting (5 items). The final draft scale contained 29 items covering four domains of the Integrated Sustainability Framework – Outer Contextual Factors (4 items), Inner Contextual Factors (9 items), Processes (5 items) and Intervention Characteristics (11 items) (see Additional file 2 for complete item list). As a result of item development processes and assessment of face validity and content validity, factors relating to the Characteristics of the Interventionist and Population domain were deemed inappropriate to be answered by ECEC service executives as they do not have a comprehensive understanding of frontline intervention delivery. As such, this domain was removed from the measure.

Phase 2: psychometric and pragmatic evaluation

Of the 1172 contacted services, 482 surveys were returned (n = 268 [57%] via telephone and n = 205 [43%] via online survey); the majority of which had full completion of measure items (n = 405 [84%]). Of the completed surveys with at least one response for measure items, 24 (5%) gave the same response for every item. Table 2 includes a breakdown of completed surveys by Australian states and territories. Surveys were completed by service executives (Nominated Supervisors n = 255 [54%], Service Directors n = 155 [33%], Room Leaders n = 11 [2.3%], and Service Owners n = 4 [0.8%]). Services cared for an average of 59 (SD = 31) children per day. For the health promotion program of interest, 241 (51%) respondents answered items based on a healthy eating program and 232 (49%) respondents answered based on a physical activity program. See Table 2 for a full description of respondent demographics and service characteristics.

Table 2 Demographic characteristics of participating early childcare services and service executives

Item investigation

Missing values were low for all 29 items, ranging from 0.8% to 3.7% (see Table 3). The full range of response options were used for 14 of the 29 items, although a left-hand skew was observed for all 29 items, with less than 5.9% of respondents utilising the lower end of the response scale, and most respondents answering towards the positive end of the scale. Polychoric correlation coefficients ranged from 0.03 to 0.77. No pairs of items recorded polychoric correlations above 0.8. However, of the 29 items, one item from the Inner Contextual Factors domain “My service would be able to continue to deliver the program if there were changes to educators at our service” possessed a high correlation (0.77) and when examined was considered conceptually similar to other items, therefore deemed appropriate to remove.

Table 3 Item-level information for the final 26-item IMPRESS-C measure

Structural validity

There were 473 participants included in the CFA model. An initial model (chi-sq = 1491, degrees of freedom [df] = 371) was run and the factor loadings and modification indices were examined for all items (see Additional file 3 for the model building process and model fit index comparisons, interfactor correlations for each CFA model, and item factor loadings). This model illustrated only one of the four model fit indices were within the pre-specified criteria for model adequacy (SRMR = 0.065; CFI = 0.831; and RMSEA = 0.080; p-value = < 0.001). One item from the Outer Contextual Factors domain “The delivery of the program has influence on the business operations/income of my service (e.g., number of child enrolments)”, exhibited a low factor loading of 0.33 and was therefore removed based on the pre-specified threshold (< 0.40). One item from the Characteristics of the Intervention domain “I believe the program has been developed by a reputable organisation” was removed based on the high modification indices and review due to cross-loadings with the Outer Contextual Factors domain (modification index = 101.9) and Processes domain (modification index = 64.3). A revised, four-factor model (chi-sq = 906, df = 293) was run which illustrated three of the four model fit indices were within the pre-specified criteria for model adequacy (SRMR = 0.056; CFI = 0.993; and RMSEA = 0.067) and indicated ‘good’ structural validity of the model according to the PAPERS scale [24]. However, the chi-square p-value was < 0.001, which was smaller than the pre-specified criteria (> 0.05). All factor loadings of the revised model were > 0.4 (see Table 3). This was the final CFA model, which resulted in 26 items being included in the final measure in the psychometric evaluation – Outer Contextual Factors (3 items), Inner Contextual Factors (8 items), Processes (5 items) and Intervention Characteristics (10 items). A one-factor model (chi-sq = 2008, df = 299) was run to compare and assess the suitability of the four-factor model. The fit indices of the one-factor model (SRMR = 0.079; CFI = 0.980; RMSEA = 0.110; p-value = < 0.001) indicated a worse fit than the four-factor model.

Floor and ceiling effects

No domains possessed > 15% of the responses at minimum nor maximum values, indicating a lack of floor and ceiling effects for all domains (Table 4).

Table 4 Domain-level results assessing the internal consistency, floor and ceiling effects, and norms


Domain scores ranged from a mean of 3.78 (SD = 0.65) (Processes domain) to 4.19 (SD = 0.43) (Outer Contextual Factors domain), and all domains possessed a median of four (see Table 4). The measure norms rated ‘good’ on the PAPERS scale [24].

Internal consistency

The Inner Contextual Factors, Processes, and Characteristics of the Intervention domains possessed ‘good’ internal consistency, with Cronbach’s alpha values between the pre-specified threshold of > 0.7 and < 0.95, ranging from 0.84 to 0.92 (see Table 4). The Outer Contextual Factors domain had a lower Cronbach’s alpha (α = 0.53).

Concurrent validity

There were statistically significant associations between the Outer Contextual Factors domain (ρ = 0.119, 95% CI: 0.02, 0.21, p = 0.017), the Inner Contextual Factors domain (ρ = 0.112, 95% CI: 0.01, 0.21, p = 0.024), and the number of years the program was delivered (Table 5). With correlation coefficients of between 0.10 and 0.29, this rated ‘emerging’ on the PAPERS scale [24].

Table 5 Domain-level results assessing concurrent validity and known groups validity

Known groups validity

There were no statistically significant relationships between the measure domains and the number of full-time staff or type of program (Table 5). With two hypotheses tested but known-groups validity failing to be detected, this rated ‘poor’ on the PAPERS scale [24].

Pragmatic qualities

Based on the PAPERS pragmatic rating, the cost of the measure is ‘excellent’ as the measure is free and in the public domain. The Flesch-Kincaid readability score for the measure was 10.6, and therefore the language was deemed ‘good’ as it was between an 8th and 12th grade level (range: 8.0–12.99). The measure has ‘excellent’ assessor burden (ease of training) as it requires no training and has free automated administration. The 26-item measure has ‘good’ length with > 10 items but ≤ 50 items. However, scoring requires manual calculation and additional inspection of response patterns or subscales, and no instructions for handling missing data are provided, which is a rating of ‘emerging’ on the PAPERS scale for assessor burden (easy to interpret) [24].


This study aimed to develop and evaluate the psychometric and pragmatic properties of the IMPRESS-C, the first known measure of sustainability determinants specific to the ECEC setting. This advances emerging work on the measurement of determinants influential to EBI sustainment in community settings [20, 27] by offering a theory-based measure informed by the Integrated Sustainability Framework [9]. A comprehensive development and evaluation process based on best practice guidelines [31, 46] was undertaken which resulted in a measure with strong content and face validity. The final 26-item IMPRESS-C was evaluated using a large national sample size for psychometric and pragmatic testing (> 350 competed surveys) [21, 65], and illustrated ‘good’ structural validity, ‘good’ internal consistency, ‘emerging’ concurrent validity, ‘poor’ known groups validity, ‘good’ norms, and ‘good’ pragmatic properties (i.e., cost, readability, length, and assessor burden – ease of training). The measure provides a novel assessment of the factors that may contribute to the sustainability of EBIs within ECEC settings from the executive-level perspective – important information to help guide policymakers and practitioners in the accurate development of strategies to target identified determinants and support EBI sustainability. However, further refinement of the measure and development of additional measures of sustainability determinants tailored to different end user perspectives (e.g., service educators [program implementers]) is needed to achieve a more holistic and comprehensive understanding of such factors.

The measure was developed using a rigorous and iterative approach based on gold standard measure development procedures [24, 46] with extensive input from a range of experts. Despite this rigorous process, we were limited by time and resource constraints and were unable to follow all aspects of the gold standard procedures, including extensive pre-testing and cognitive interviews with the target population. Future measure development studies in this setting should strive to conduct cognitive interviews with a separate sample of the target population to provide granular feedback on item comprehension, enhance response processes and ensure a more robust assessment of face and content validity [21, 66]. Further, this measure should be complemented with additional measures of sustainability determinants at the level of implementer or frontline intervention delivery to obtain a full range of perspectives within this specific setting to assess additional constructs important to sustainability e.g., motivation, self-efficacy, skill acquisition, and perceived individual benefits and stressors [20, 25]. This would also facilitate a more comprehensive and accurate understanding of the determinants important to the sustainability of EBIs in the ECEC setting and inform the development and tailoring of strategies to support intervention sustainment.

The measure illustrated ‘good’ internal consistency according to the PAPERS scale [24], with Cronbach’s alpha values for three of the four domains falling between the pre-specified threshold (Inner Contextual Factors, Processes and Intervention Characteristics). This is indicative that measurement reliability for these three domains is high. However, Outer Contextual Factors possessed a lower Cronbach’s alpha value which may be attributed to the lower number of items covered under that domain in comparison to the others [67]. To improve this for future research, focus should be placed on the creation, refinement and testing of additional items within this domain to yield higher internal consistency, provided such items remain relevant to external or Outer Contextual Factors.

Assessment of structural validity found three of the four model fit indices were within the pre-specified criteria indicating ‘good’ structural validity of the model according to the PAPERS scale [24]. This demonstrates that the measure accurately reflects the underlying structure or constructs it intends to assess (i.e., domains and constructs of the Integrated Sustainability Framework). However, we failed to meet the chi-square p-value criteria of > 0.05. The chi-square test is a difficult criteria to meet and is quite sensitive particularly when applied to moderate to large sample sizes, therefore, it is more likely to detect small differences that may not have as big an impact. This solidifies the need to have multiple indices to assess structural validity against. Further, although the measure was theoretically informed by the Integrated Sustainability Framework [9] and included constructs that reflect the main determinants found to influence EBI sustainability from the perspective of the service executive, we only included four of the five framework domains. Thus, it does not capture sustainability determinants associated with frontline intervention delivery and implementation. Again, highlighting the need for additional measures to assess these characteristics from the implementer perspective [20, 25].

For the assessment of known-groups validity, we found no evidence to support our initial hypotheses i.e., no statistically significant relationships between the framework domains and the number of full-time staff, nor the type of program. Potential reasons for our hypotheses not being supported could be a lack of difference in determinants between nutrition and physical activity programs as these are both important and related health behaviours often targeted simultaneously in EBI delivery [29]. Therefore, it is possible they possess the same or similar determinants for sustainability. It is important to assess the ability of the measure to accurately discriminate between groups that are expected to have distinct levels or characteristics on the construct of interest [21]. Based on these findings, it may be necessary to revisit the hypotheses, to obtain more robust evidence for known groups validity for the measure. However, given the lack of empirical evidence available that informs where the differences may lie, it is difficult to determine what known groups may exist at this time.

Examination of concurrent validity found a statistically significant relationship between the Outer Contextual Factors domain (p = 0.017) (e.g., external partnerships, socio-political support), the Inner Contextual Factors domain (p = 0.024) (e.g., organisational readiness and resources, executive leadership and support, workforce turnover), and the number of years the program was delivered. This exhibited a strong positive relationship between these domains of sustainability determinants and the length of program delivery, which is an expected finding given the length of program delivery is a primary indicator of its sustainment as intended (i.e., the longer a program is delivered, the longer it is sustained) [68]. Given the limited research in this area and lack of gold standard measurement into sustainability and sustainability determinants, there are limited other measures and constructs that could be confidently used to assess additional forms of validity for the IMPRESS-C. As the field progresses and researchers become more aware of determinants impacting intervention sustainability, we recommend future research continues to assess and improve the validity of the IMPRESS-C measure.

Although responsiveness was unable to be assessed, examination of floor and ceiling effects, which are indicators of this, was conducted to ensure the potential for the IMPRESS-C to detect change [49]. No domains possessed floor or ceiling effects, with < 15% of the responses at minimum and maximum values. However, only 5.9% of respondents answered the low end of the response scale. To mitigate this, strategies are needed such as reviewing and testing different response scales (i.e., with varying response options and number of options); and increasing item difficulty so that it better reflects the higher end of the response scale to be captured, potentially making the measure more sensitive to change [69].

The IMPRESS-C possessed an ‘excellent’ PAPERS rating for cost by ensuring the measure is in the public domain, a ‘good’ language rating by ensuring the readability of the measure was between an 8th and 12th grade level, ‘good’ length by ensuring the measure possessed < 50 items, and ‘excellent’ ease of training as it required no training and had free automated administration [24]. These qualities provide a highly pragmatic and user-friendly measurement tool for researchers to capture the priority executive-level determinants impacting on EBI sustainment within the ECEC setting [70]. Despite the good pragmatic qualities of the measure, further refinement to reduce assessor burden could be achieved by providing clear cut-off scores with value labels, instructions for handling missing data, and automated calculation of measure scores.


The IMPRESS-C possesses good psychometric and pragmatic qualities for assessing executive-level perceptions of determinants influencing sustainment of public health interventions in the ECEC setting. Future efforts should be directed at refining this measure to further improve its psychometric and pragmatic properties, and complementing this measure with a valid and reliable measure of sustainability determinants targeting frontline intervention delivery staff. This would enable understanding of a range of perspectives among key end-users responsible for the delivery and governance of EBIs in ECEC settings and help inform a comprehensive and tailored approach to developing strategies supporting EBI sustainment within the setting.

Availability of data and materials

Data and materials are available from the corresponding author on reasonable request.



Australian Children's Education & Care Quality Authority


Computer-assisted telephone interview


Confirmatory factor analysis


Comparative Fit Index


COnsensus-based Standards for the selection of health status Measurement INstruments


Evidence-based intervention


Early childhood education and care


Integrated Measure of PRogram Element SuStainability in Childcare Settings


Psychometric and Pragmatic Evidence Rating Scale


Outside of school hours


Root Mean Square Error of Approximation


Standardized Root Square Residual


  1. Wolfenden L, Nathan NK, Sutherland R, Yoong SL, Hodder RK, Wyse RJ, et al. Strategies for enhancing the implementation of school-based policies or practices targeting risk factors for chronic disease. Cochrane Database Syst Rev. 2017;11(11):CD011677.

    Article  PubMed  Google Scholar 

  2. Matwiejczyk L, Mehta K, Scott J, Tonkin E, Coveney J. Characteristics of effective interventions promoting healthy eating for pre-schoolers in childcare settings: an umbrella review. Nutrients. 2018;10(3):293.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Wolfenden L, Barnes C, Jones J, Finch M, Wyse RJ, Kingsland M, et al. Strategies to improve the implementation of healthy eating, physical activity and obesity prevention policies, practices or programmes within childcare services. Cochrane Database of Syst Rev. 2020;2(2):CD011779.

    Article  Google Scholar 

  4. Lum M, Wolfenden L, Jones J, Grady A, Christian H, Reilly K, Yoong SL. Interventions to improve child physical activity in the early childhood education and care setting: an umbrella review. Int J Environ Res Public Health. 2022;19(4):1963.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yoong SL, Lum M, Wolfenden L, Jackson J, Barnes C, Hall AE, et al. Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years. Cochrane Database of Syst Rev. 2023;6(6):CD013862.

    Article  CAS  Google Scholar 

  6. WiltseyStirman S, Kimberly J, Cook N, Calloway A, Castro F, Charns M. The sustainability of new programs and innovations: a review of the empirical literature and recommendations for future research. Implement Sci. 2012;7(1):1–19.

    Article  Google Scholar 

  7. Herlitz L, MacIntyre H, Osborn T, Bonell C. The sustainability of public health interventions in schools: a systematic review. Implement Sci. 2020;15(1):1–28.

    Article  Google Scholar 

  8. Scheirer MA, Dearing JW. An agenda for research on the sustainability of public health programs. Am J Public Health. 2011;101(11):2059–67.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shelton RC, Cooper BR, Stirman SW. The sustainability of evidence-based interventions and practices in public health and health care. Annu Rev Public Health. 2018;39:55–76.

    Article  PubMed  Google Scholar 

  10. Bodkin A, Hakimi S. Sustainable by design: a systematic review of factors for health promotion program sustainability. BMC Public Health. 2020;20(1):964.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Moore JE, Mascarenhas A, Bain J, Straus SE. Developing a comprehensive definition of sustainability. Implement Sci. 2017;12(1):1–8.

    Article  Google Scholar 

  12. Luke DA, Calhoun A, Robichaux CB, Elliott MB, Moreland-Russell S. The program sustainability assessment tool: a new instrument for public health programs. Prev Chronic Dis. 2014;11: 130184.

    Article  PubMed  Google Scholar 

  13. Birken SA, Haines ER, Hwang S, Chambers DA, Bunger AC, Nilsen P. Advancing understanding and identifying strategies for sustaining evidence-based practices: a review of reviews. Implement Sci. 2020;15:88.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cassar S, Salmon J, Timperio A, Naylor PJ, van Nassau F, Contardo Ayala AM, et al. Adoption, implementation and sustainability of school-based physical activity and sedentary behaviour interventions in real-world settings: a systematic review. Int J Behav Nutr Phys Act. 2019;16(1):120.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Shoesmith A, Hall A, Wolfenden L, Shelton RC, Powell BJ, Brown H, et al. Barriers and facilitators influencing the sustainment of health behaviour interventions in schools and childcare services: a systematic review. Implement Sci. 2021;16:62.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chambers DA, Glasgow RE, Stange KC. The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change. Implement Sci. 2013;8(1):1–11.

    Article  Google Scholar 

  17. Schell SF, Luke DA, Schooley MW, Elliott MB, Herbers SH, Mueller NB, et al. Public health programs capacity for sustainability: a new framework. Implement Sci. 2013;8:15.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hodge LM, Turner KMT. Sustained implementation of evidence-based programs in disadvantaged communities: a conceptual framework of supporting factors. Am J Community Psychol. 2016;58(1–2):192–210.

    Article  PubMed  Google Scholar 

  19. Seward K, Wolfenden L, Wiggers J, Finch M, Wyse R, Oldmeadow C, et al. Measuring implementation behaviour of menu guidelines in the childcare setting: confirmatory factor analysis of a theoretical domains framework questionnaire (TDFQ). Int J Behav Nutr Phys Act. 2017;14:45.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hall A, Shoesmith A, Doherty E, McEvoy B, Mettert K, Lewis CC, et al. Evaluation of measures of sustainability and sustainability determinants for use in community, public health, and clinical settings: a systematic review. Implement Sci. 2022;17:81.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health. 2018;6:149.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Terwee CB, Prinsen CAC, Chiarotto A, Westerman MJ, Patrick DL, Alonso J, et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res. 2018;27(5):115–1170.

    Article  Google Scholar 

  23. Hull L, Boulton R, Jones F, Boaz A, Sevdalis N. Defining, conceptualizing and evaluating pragmatic qualities of quantitative instruments measuring implementation determinants and outcomes: a scoping and critical review of the literature and recommendations for future research. Transl Behav Med. 2022;12(11):1049–64.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lewis CC, Mettert KD, Stanick CF, Halko HM, Nolen EA, Powell BJ, Weiner BJ. The psychometric and pragmatic evidence rating scale (PAPERS) for measure development and evaluation. Implement Res Pract. 2021;10(2):26334895211037390.

    Article  Google Scholar 

  25. Moullin JC, Sklar M, Green A, Dickson KS, Stadnick NA, Reeder K, et al. Advancing the pragmatic measurement of sustainment: a narrative review of measures. Implement Sci Commun. 2020;1:76.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lewis CC, Fischer S, Weiner BJ, Stanick C, Kim M, Martinez RG. Outcomes for implementation science: an enhanced systematic review of instruments using evidence-based rating criteria. Implement Sci. 2015;10:155.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Mettert K, Lewis C, Dorsey C, Halko H, Weiner B. Measuring implementation outcomes: an updated systematic review of measures’ psychometric properties. Implement Res Pract. 2020;1:1–29.

    Article  Google Scholar 

  28. Moullin JC, Sklar M, Ehrhart MG, Green A, Aarons G. Provider REport of Sustainment Scale (PRESS): development and validation of a brief measure of inner context sustainment. Implement Sci. 2021;16:86.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Chamberlain P, Hendricks Brown C, Saldana L. Observational measure of implementation progress in community based settings: the Stages of Implementation Completion (SIC). Implement Sci. 2011;6:116.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Palinkas LA, Chou CP, Spear SE, Mendon SJ, Villamar J, Brown CH. Measurement of sustainment of prevention programs and initiatives: the sustainment measurement system scale. Implement Sci. 2020;15(1):71.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Stanick CF, Halko HM, Nolen EA, Powell BJ, Dorsey CN, Mettert KD, et al. Pragmatic measures for implementation research: development of the Psychometric and Pragmatic Evidence Rating Scale (PAPERS). Transl Behav Med. 2021;11(1):11–20.

    Article  PubMed  Google Scholar 

  32. McIntosh K, MacKay LD, Hume AE, Doolittle J, Vincent CG, Horner RH, et al. Development and initial validation of a measure to assess factors related to sustainability of school-wide positive behavior support. J Posit Behav Interv. 2010;13(4):208–18.

    Article  Google Scholar 

  33. Malone S, Prewitt K, Hackett R, Lin JC, McKay V, Walsh-Bailey C, et al. The clinical sustainability assessment tool: measuring organizational capacity to promote sustainability in healthcare. Implement Sci Commun. 2021;2(1):77.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kittelman A, Mercer SH, McIntosh K, Nese RNT. Development and validation of a measure assessing sustainability of tier 2 and 3 behavior support systems. J Sch Psychol. 2021;85:140–54.

    Article  PubMed  Google Scholar 

  35. Hodge LM, Turner KMT, Sanders MR, Filus A. Sustained implementation support scale: validation of a measure of program characteristics and workplace functioning for sustained program implementation. J Behav Health Serv Res. 2017;44(3):442–64.

    Article  PubMed  Google Scholar 

  36. Ehrhart MG, Torres EM, Green AE, Trott E, Willging CE, Moullin JC, et al. Leading for the long haul: a mixed-method evaluation of the Sustainment Leadership Scale (SLS). Implement Sci. 2018;13:17.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Mancini JA, Marek LI. Sustaining community-based programs for families: conceptualisation and measurement. Fam Relat. 2004;53(4):339–47.

    Article  Google Scholar 

  38. Williams RM, Zhang J, Woodard N, Slade JL, Santos LZ, Knott CL. Development and validation of an instrument to assess institutionalization of health promotion in faith-based organizations. Eval Program Plann. 2020;79:101781.

    Article  PubMed  Google Scholar 

  39. Solberg LI, Asche SE, Margolis KL, Whitebird RR. Measuring an organization’s ability to manage change: the change process capability questionnaire and its use for improving depression care. Am J Med Qual. 2008;23(3):193–200.

    Article  PubMed  Google Scholar 

  40. The Board of Regents of the University System of Georgia by and on behalf of Georgia State University and the Georgia Health Policy Center. Positioning for sustainability: a formative assessment tool – quick course. 2011.

  41. Office of Adolescent Health. Building sustainable programs: the resource guide. 2014.

    Google Scholar 

  42. Maher L, Gustafson DH, Evans A. Sustainability model and guide. 2010.

    Google Scholar 

  43. OECD. Enrolment in childcare and pre-schools. Available at: Accessed 12 June 2023.

  44. Scheirer MA. Linking sustainability research to intervention types. Am J Public Health. 2013;103(4):e73–80.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Asada Y, Lin S, Siegel L, Kong A. Facilitators and barriers to implementation and sustainability of nutrition and physical activity interventions in early childcare settings: a systematic review. Prev Sci. 2023;24(1):64–83.

    Article  PubMed  Google Scholar 

  46. Mokkink LB, Terwee CB, Knol DL, Stratford PW, Alonso J, Patrick DL, et al. Protocol of the COSMIN study: COnsensus-based Standards for the selection of health Measurement INstruments. BMC Med Res Methodol. 2006;6:2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Finch TL, Girling M, May CR, Mair FS, Murray E, Treweek S, et al. Improving the normalization of complex interventions: part 2 - validation of the NoMAD instrument for assessing implementation work based on normalization process theory (NPT). BMC Med Res Methodol. 2018;18(1):135.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Rapley T, Girling M, Mair FS, Murray E, Treweek S, McColl E, et al. Improving the normalization of complex interventions: part 1 - development of the NoMAD instrument for assessing implementation work based on normalization process theory (NPT). BMC Med Res Methodol. 2018;18(1):133.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60(1):34–42.

    Article  PubMed  Google Scholar 

  50. Yoong SL, Pearson N, Reilly K, Wolfenden L, Jones J, Nathan N, et al. A randomised controlled trial of an implementation strategy delivered at scale to increase outdoor free play opportunities in early childhood education and care (ECEC) services: a study protocol for the get outside get active (GOGA) trial. BMC Public Health. 2022;22:610.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Rosseel Y. lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48(2):1–36.

    Article  Google Scholar 

  52. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2022.

  53. O’Rourke N, Hatcher L. A step-by-step approach to using SAS® for factor analysis and structural equation modeling. 2nd ed. Cary: SAS Institute Inc.; 2013.

    Google Scholar 

  54. Fabrigar LR, Wegener DT, MacCallum RC, Strahan E. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999;4(3):272–99.

    Article  Google Scholar 

  55. Mîndrilă D. Maximum Likelihood (ML) and Diagonally Weighted Least Squares (DWLS) estimation procedures: a comparison of estimation bias with ordinal and multivariate non-normal data. IJDS. 2010;1(1):60–6.

    Article  Google Scholar 

  56. Li CH. Confirmatory factor analysis with ordinal data: comparing robust maximum likelihood and diagonally weighted least squares. Behav Res. 2016;48:936–49.

    Article  Google Scholar 

  57. Harrell Jr F & Dupont CH. Hmisc: Harrell Miscellaneous. R Package Version 4.2–0. 2019.

  58. Kline RB. Principles and practice of structural equation modeling. New York: Guilford Press; 2005.

    Google Scholar 

  59. Weiner BJ, Lewis CC, Stanick C, Powel BJ, Dorsey C, Clary A, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12(1):108.

    Article  PubMed  PubMed Central  Google Scholar 

  60. DeVellis RF. Scale development: theory and application. Los Angeles: Sage; Publications; 2012.

    Google Scholar 

  61. Hooper D, Coughlan J, Mullen M. Structural equation modelling: guidelines for determining model fit. Electron J Bus Res Methods. 2007;6(1):53–60.

    Google Scholar 

  62. Lin WL, Yao G. Concurrent Validity. In: Michalos AC, editor. Encyclopedia of quality of life and well-being research. Dordrecht: Springer; 2014.

    Chapter  Google Scholar 

  63. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. 2018;18(3):91–3.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Hailemariam M, Bustos T, Montgomery B, Barajas R, Evans LB, Drahota A. Evidence-based intervention sustainability strategies: a systematic review. Implement Sci. 2019;14:57.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Comfrey AL, Lee HB. A First Course in Factor Analysis. Hillsdale: Lawrence Erlbaum Associates; 1992.

    Google Scholar 

  66. García AA. Cognitive interviews to test and refine questionnaires. Public Health Nurs. 2011;28(5):444–50.

    Article  PubMed  Google Scholar 

  67. Souza AC, Alexandre NMC, Guirardello EB. Psychometric properties in instruments evaluation of reliability and validity. Epidemiol Serv Saude. 2017;26(3):649–59. English, Portuguese.

    Article  PubMed  Google Scholar 

  68. Lennox L, Maher L, Reed J. Navigating the sustainability landscape: a systematic review of sustainability approaches in healthcare. Implement Sci. 2018;13:27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Bruce B, Fries J, Lingala B, Hussain YN, Krishnan E. Development and assessment of floor and ceiling items for the PROMIS physical function item bank. Arthritis Res Ther. 2013;15(5):R144.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Glasgow RE, Riley WT. Pragmatic measures: what they are and why we need them. Am J Prev Med. 2013;45(2):237–43.

    Article  PubMed  Google Scholar 

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The authors would like to acknowledge members of the advisory group that helped develop the measure and assess face and content validity.


This project is funded through the National Health and Medical Research Council (NHMRC). AS is supported by a University of Newcastle PhD scholarship (ref. 3145402). NN is supported by a MRFF Investigator Grant (APP1194785); LW is supported by an NHMRC Investigator Grant (APP1197022); RCS by an American Cancer Society Research Scholar Grant (RSG-17–156-01-CPPB); and SY by an Australia Research Council Discovery Early Career Research Awards (DE170100382). The funders had no role in the study design, conduct of the study, analysis, or dissemination of findings.

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AS, AH and NN led the study conceptualisation and design. SY, NN and LW acquired funding for the study. AS and AH led the planning for measure development and evaluation processes. RCS, LW, SY, and BC provided expert advice on measure development. ML, NI, AG and NP provided expertise from the ECEC setting contributing to measure development and refinement. SY, ML and AG coordinated survey data collection. EN conducted the formal statistical analyses. AS and AH led interpretation of the results. AS led drafting of the manuscript. AS, AH, NN, ML, SY, EN, LW, RCS, BC, ERG, CL, AG, NI, NM, and NP have reviewed, edited and agreed to the published version of the manuscript.

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Correspondence to Adam Shoesmith.

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We sought approval for this study from the Hunter New England Human Research Ethics Committee (no. 06/07/26/4.04 2019/ETH12353) and University of Newcastle Human Research Ethics Committee (no. H-2008–0343).

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Shoesmith, A., Nathan, N., Lum, M. et al. Integrated Measure of PRogram Element SuStainability in Childcare Settings (IMPRESS-C): development and psychometric evaluation of a measure of sustainability determinants in the early childhood education and care setting. Implementation Sci 19, 41 (2024).

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