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Abstract

Content validity describes the extent to which a measure represents, and is relevant to, the construct it aims to assess. The International Classification of Functioning, Disability and Health and derived Core/Code Sets (Sets) for autism, attention deficit-hyperactivity disorder, cerebral palsy and early developmental delay and disability are adequate to establish the content validity of measures aiming to assess functioning in young children with neurodevelopmental conditions (NDCs). This article aimed to assess the content validity of comprehensive assessments of functioning for young children with NDCs against these standards. Twenty-two common measures of functioning were evaluated for content validity against the International Classification of Functioning, Disability and Health at a domain level, with 10 measures analysed at the item-level and compared to the Sets relevant to young children with NDCs. Measures covered between 21% and 57% of the combined Set codes and 19% to 63% of codes from specific Sets. Much of this variation was between measures, with some variation due to differences between individual Sets. The percentages reflect that measures heavily focus on activities and participation areas, with environmental factors rarely assessed. These findings are useful for clinicians, policymakers, and researchers in identifying the most appropriate measures for assessing functioning in young children with neurodevelopmental conditions.

Lay abstract

Young children who have developmental delay, autism, or other neurodevelopmental conditions can have difficulties doing things in different areas of their life. What they can and cannot do is called their level of functioning. There are lots of assessment measures that aim to assess functioning. But, we are not sure if these measures assess all the things we need to know about these children’s functioning. Other research has identified lists of items (codes) that need to be assessed to understand functioning for young children with different neurodevelopmental conditions fully. These lists include body functions (the things a child’s body or brain can do), activities and participation (the activities and tasks a child does) and environmental factors (parts of the environment that can influence functioning). In this study, we looked at the items from these lists assessed by different functioning measures to see how they compared to what should be assessed. The measures that we looked at covered 21%–57% of all the codes and 19%–63% of the codes for lists specific to different conditions. Most of the measures focused on activity and participation codes, and they rarely assessed environmental factors. Knowing which codes and how much of the lists the measures assess can help researchers, clinicians and policymakers to choose measures that are more appropriate for young children with neurodevelopmental conditions.

Keywords

adaptive behavior attention-deficit/hyperactivity disorder autism spectrum disorders behavioral measurement cerebral palsy content validity developmental delay functioning

Introduction

Children with neurodevelopmental conditions and delays (NDCs), including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), developmental coordination disorder (DCD) and global developmental delay (GDD), vary widely regarding their skills and abilities for functioning in everyday life (Ashwood et al., 2015; Licari et al., 2019, 2020; Riou et al., 2009). Being able to accurately and comprehensively assess the functioning of children with NDCs is an essential part of diagnostic assessments, intervention planning, and funding processes (Commonwealth of Australia, 2013; Miltenberger et al., 2016; Msall, 2005; National Institute for Health and Care Excellence, 2011; Whitehouse et al., 2018). It is important for assessment, and subsequent intervention provision, to occur as early as possible to take advantage of critical windows for brain development in the years before preschool (Spittle et al., 2012). Preschool children who have received an NDC diagnosis, and those who may not yet received a diagnosis (or will go on to receive additional diagnoses), both require comprehensive and trans-diagnostic assessments of functioning, as symptomology and functional impairment may not be directly related (Alvares et al., 2020; Whitehouse et al., 2018; Zander & Bölte, 2015).

Measures of functioning are commonly classified according to their aims and theoretical underpinnings, such as measures of ‘adaptive behaviour’ (e.g. Vineland-3; Sparrow, Cicchetti, & Saulnier, 2016), or ‘occupational performance’ (e.g. Canadian Occupational Performance Measure; Law et al., 2005). It should be noted that these concepts may be defined differently between contexts and over time (Price et al., 2018). In addition to their clinical use, these measures are frequently used as outcome measures in intervention studies for children with NDCs (McConachie et al., 2015; Whitehouse et al., 2020).

Content validity is defined according to the Consensus-based standards for the selection of health measurement instruments taxonomy as ‘the degree to which the content of [a measure] is an adequate reflection of the construct to be measured’ (Mokkink et al., 2010, p. 743). While psychometric studies of these measures often address other properties, content validity is rarely examined but implicitly assumed despite its fundamental importance (McConachie et al., 2015; Price et al., 2018). A systematic review of outcome measures for young children on the autism spectrum by McConachie et al. (2015) found no studies reviewing the content validity of measures of global functioning or daily living skills. This paucity of literature could be due to the absence of a widely accepted conceptual understanding of functioning in young children. The conceptual domains of adaptive behaviour, the most common theoretical construct underpinning these measures, are historically defined and remain the subject of ongoing debate (Price et al., 2018).

In recent years, a transdisciplinary conceptual framework, in the form of the International Classification of Functioning, Disability and Health (ICF) Core Sets and Code Sets has been supported and endorsed by the World Health Organisation (Selb et al., 2015). The ICF is a comprehensive and widely utilised biopsychosocial framework of functioning, shaping many national and international health, disability and education policies (Madden & Bundy, 2019; World Health Organization [WHO], 2007). The ICF promotes a holistic view of functioning acknowledging the influence of, and interaction between, an individual’s ‘body functions and structures’, ‘activities and participation’, ‘environmental factors’, ‘personal factors’ and ‘health conditions’ (WHO, 2007). The ICF framework, designed to be transdisciplinary, mirrors common occupational therapy models and, to some extent, definitions of adaptive behaviour (Maritz et al., 2018; Price et al., 2018). As well as the overarching framework, the ICF defines 1685 codes classifying functioning across the body functions, body structures, activities and participation and environmental factor domains (WHO, 2007). Personal factors have no official codes or classifications, with the concept of classifying and rating personal factors in the same manner as other ICF components viewed as antithetical to the purpose of the ICF (Leonardi et al., 2015; WHO, 2007). Specific codes relevant to the developing child were added to the ICF children and youth version (ICF-CY) but have now been absorbed into the main ICF document (WHO, 2012). The codes are nested, with level-1 codes (chapters) providing the broadest definitions and codes increasing in detail to the second, third or fourth levels. An overview of the structure of the ICF, its components, domains, chapters and codes are provided in Supplementary File 1.

The ICF Core Sets are shortlists of ICF codes relevant to particular health conditions and ICF Code Sets are those relevant to specific life stages or situations. Several ICF Core Sets are relevant for young children with NDCs: those for young children (aged 0–5 years) with ADHD (Bölte et al., 2018), ASD (Bölte et al., 2019) and cerebral palsy (CP; Schiariti et al., 2015). There is also an ICF Code Set for Early Delay and Disabilities (EDDs; Pan et al., 2015). The purpose of ICF Core and Code Sets (Sets) is to facilitate the use of the ICF in clinical practice (Bölte et al., 2014). Underpinning the development of the Sets is a standardised, multi-stage research process ensuring their content validity, making them an ideal standard by which to assess content validity of measures of functioning (Kielhofner, 2006; Mahdi et al., 2018; Selb et al., 2015). Therefore, this article aims to investigate the content validity of the most comprehensive measures of functioning in young children with NDCs in reference to the standard of the ICF and the relevant Sets. This understanding will assist clinicians and researchers in evaluating the suitability of these measures in measuring the functioning in young children with NDCs.

Methods

Grey literature search and comparison to ICF chapters

A grey literature search was conducted (Figure 1), with 1051 measures for consideration extracted from four books and a systematic review (Asher, 2014; Law et al., 2017; Majnemer, 2012; McConachie et al., 2015; Mulligan, 2014). A grey literature search was chosen over a review of the literature, to capture established measures available for clinical use and exclude those still in development. The sources selected were key works relevant to measures of functioning, identified through clinical experience and knowledge of the literature, including consultation with Curtin University’s School of Allied Health’s resource library.

Figure 1.

Screening process to identify measures.

Prior to the search, inclusion criteria were determined to identify measures that were (a) relevant to the ICF’s concept of functioning; (b) comprehensive (i.e. exclude measures only targeting one area of functioning); (c) appropriate for the target age group and (d) available for clinical use. It was expected that the criteria would need to be modified based on the initial results to capture measures congruent with the aim of the research. Once measures were extracted, duplicate results were removed leaving 793 measures. The following final inclusion criteria were determined and then applied: measures needed to (a) have a primary aim related to at least one domain of the ICF: body functions, activities and participation or environmental factors; (b) cover at least two chapters of the ICF; (c) not be a screening tool; (d) be stated as appropriate for individuals with NDCs or the general population; (e) be applicable for more than 50% of the 0–5 years and 11 months years age group and (f) be published in 2000 or later and be available for download or purchase at the time of data collection (2020).

Twenty-two measures met the inclusion criteria and were analysed by two researchers using information available on publishers’ websites (e.g. domains of the measure) to determine how many chapters of the ICF they covered. Once this was completed, an inclusion criterion for item-level linking of eight or more chapters of the ICF was decided, in order to select the most comprehensive measures. Ten measures met this standard and were included for full content validation (Supplementary File 2). Where measures had versions with different questions, these were assessed as individual measures (e.g. Comprehensive and Domain versions). Therefore, the Vineland-3 comprehensive and domain-level forms, and PEDI-CAT original and PEDI-CAT (ASD), both with and without the mobility device domain were considered individually. This resulted in 14 measures included for detailed analysis are presented in Table 1.

Table 1.

Measures analysed for content validity.

Abbreviation	Measure	Number of items	Reference
ABAS-3	Adaptive Behaviour Assessment System, 3rd Ed.	241	Harrison and Oakland (2015)
LIFE-H 0.3	Assessment of Life Habits 3.0	61	International Network on the Disability Creation Process (2005)
Bayley-4	Bayley Scales of Infant Development, 4th Ed.	419	Bayley and Aylward (2020)
DAYC-2	Developmental Assessment of Young Children, 2nd Ed.	380	Voress et al. (2012)
DP-4	Developmental Profile-4	190	Alpern (2020)
M-P-R	Merrill Palmer Revised Scales of Development	749	Roid and Sampers (2004)
PEDI-CAT	Paediatric Evaluation of Disability Inventory Computer Adaptive Test	242	Haley et al. (2019)
(MD)	with Mobility Device	189
(ASD)	Autism Spectrum Disorder Version	277
(ASD, MD)	Autism Spectrum Disorder Version, with Mobility Device	224
SCOPE	Short Child Occupational Profile	27	Bowyer et al. (2008)
Vineland-3	Vineland Adaptive Behaviour Scales, 3^rd Ed.		Sparrow, Saulnier, et al. (2016)
Comp	Comprehensive Version	502
Dom	Domain Level Version	195
YC-PEM	Young Children’s Participation and Environment Measure	73	Khetani et al. (2013)

Full linking and comparison to ICF and ICF Sets

The content validity of the measures was evaluated by mapping items against the ICF according to the established ICF linking methodology (Cieza et al., 2019). This methodology allows the content of qualitative data sources to be compared to the ICF and any ICF Sets. For this study, measures were compared to the Sets for young children (0–5 years) with ASD (Bölte et al., 2019), ADHD (Bölte et al., 2018), CP (Schiariti et al., 2015) and EDD (Pan et al., 2015), and a combined ‘Early Neurodevelopmental’ (eND) Set generated by the authors by combining the items of the above Sets.

Questions from each measure were extracted from the manual or form and entered into a Microsoft Excel template. Two reviewers (E.D. and K.W.) who had completed training relating to the ICF and linking methodology, assessed the meaningful concepts of each item, including examples, and linked these to the ICF (Cieza et al., 2019). A small sample of items was discussed between the two reviewers to facilitate consistency of approach before linking all items of the measures. The measures’ perspectives and response options were reviewed and classified as part of the linking process and considered when determining meaningful concepts (Cieza et al., 2019). Where applicable, ‘non-specified’ and ‘other specified’ codes were used. If a meaningful concept was not relevant to the ICF, it was classified as ‘not covered’ and if was covered by the ICF but did not provide sufficient detail to support linking to a code, it was classified as ‘not definable’. After linking the measures’ items independently, the reviewers met to discuss any discrepancies and reach a consensus for each measure. If agreement could not be reached, a third reviewer (A.C.) reviewed the items and the ICF to reach a final consensus. Once the consensus process was completed, reviewer agreement was 100%. This produced a final list of codes (from levels two, three and four of the ICF) that described the content of each measure.

For each measure, frequency distributions were made for each ICF code to summarise their content. The content of the measures compared to the ICF domains and chapters was assessed using these frequency distributions. Before assessing the measures’ content validity using the Sets, the lists of codes were simplified by reducing any level three or four codes to their corresponding second-level ‘parent’ code. From these level two codes, frequency distributions for codes included in the 0–5 years ASD, 0–5 years ADHD, 0–5 years CP, EDD and eND Sets were calculated for each measure to identify the percentage of the Sets they assessed. This percentage was calculated for each overall Set and for the codes from each ICF domain within the Set.

Community involvement

Two of the authors of this article are parents to autistic children. There was no other involvement from the autistic or autism communities in this research.

Results

Full linking and comparison to ICF and eND, ASD, CP and EDD Sets

Perspectives of measures

The perspectives and response options of the measures evaluated varied (Supplementary File 3), with all taking a descriptive perspective for at least one question or domain. Measures taking a descriptive perspective most frequently assessed performance (‘what an individual does in his or her given environment’; Cieza et al., 2019, p. 579), with the exception of those including a standardised clinician-administered component, which instead assessed capacity (‘individual’s ability to execute a task or action . . . [in] a standardised environment’; WHO, 2007, pp. 13). Other perspectives included the need for or dependency on supports (PEDI-CAT and LIFE-H), and appraisal of satisfaction with current performance (LIFE-H). Response options usually included confirmation/agreement (yes/no if a child performs a behaviour), or the frequency that the child performs a behaviour, with the ABAS-3 scale combining the two. Other less-common response options were intensity (how difficult a task was for the child or the level of satisfaction with the performance), qualitative responses describing different behaviours (Bayley-4) or the type of impact an environmental factor had on participation (YC-PEM).

Content validity compared to the ICF domains and chapters

Results showed that for all measures, with the exception of the YC-PEM, the majority of items fell under the activities and participation domain (range = 38%–96%, Mdn = 87%). Very rarely were questions related to environmental factor codes, with only two measures evaluating this domain: the SCOPE (26% of codes) and YC-PEM (49% of codes). The remainder of the codes fell under the body functions domain (Mdn = 11%, range = 4%–34%). Within the different domains, the distribution of codes across the chapters was not consistent.

For items coded as body functions, the majority of items fell under the ‘Mental functions’ chapter (range = 50%–95%, Mdn = 81%), with the next most common being the ‘Neuromusculoskeletal and movement related functions’ (range = 0%–37%, Mdn = 3%). All other body function chapters were rarely assessed. The activities and participation chapters were more consistently assessed than the body function chapters, however there was still moderate variability between the distribution of codes. While ‘Mobility’, ‘Learning and applying knowledge’ and ‘Self-care’ chapters shared the highest median of coverage (18%), measures ranged of coverage varied widely (range = 5%–49%, 6%–36%, and 6%–26%, respectively). ‘Communication’ items were also frequently assessed (range = 3%–33%, Mdn = 13%). The chapters ‘Major life areas’ and ‘Domestic life’ were the least commonly assessed, with some measures failing to assess theses chapters at all (range = 0%–11%, Mdn = 3% and range = 0%–12%, Mdn = 5%, respectively). Only the SCOPE and YC-PEM assessed environmental factors, most frequently assessing ‘Products and technology’ (Mdn = 34%) and ‘Support and relationships’ (Mdn = 25%) and less frequently ‘Attitudes’ (Mdn = 12%). The ‘Natural environment and human made changes to the environment’ was only covered by the YC-PEM.

The distribution of codes from the measures across the domains and chapters of the ICF is outlined in Table 2. The distribution of ICF Codes across each measure and its domains is provided in Supplementary File 4.

Table 2.

Distribution of codes assigned to measures across the ICF domains and chapters.

Component of ICF		ABAS-3	Bayley-4	DAYC-2	DP4	LIFE-H 3.0	M-P-R	PEDI-CAT	PEDI-CAT (MD)	PEDI-CAT (ASD)	PEDI-CAT (ASD, MD)	SCOPE	Vineland-3 Comp	Vineland-3 Domain	YC-PEM
Total codes		325	617	531	264	77	997	270	213	315	258	57	648	253	149
Body functions (BF)	Codes	32	97	94	17	3	336	17	17	19	19	6	117	70	19
	%Total ICF	10%	16%	18%	6%	4%	34%	6%	8%	6%	7%	11%	18%	28%	13%
Mental	Codes	22	71	54	15	2	169	16	16	18	18	4	107	65	10
	% BF Domain	69%	73%	57%	88%	67%	50%	94%	94%	95%	95%	67%	91%	93%	53%
Sensory and pain	Codes	0	7	5	1	0	34	0	0	0	0	0	4	2	0
	% BF Domain	0%	7%	5%	6%	0%	10%	0%	0%	0%	0%	0%	3%	3%	0%
Voice and speech	Codes	3	5	12	0	0	9	0	0	0	0	0	1	1	0
	% BF Domain	9%	5%	13%	0%	0%	3%	0%	0%	0%	0%	0%	1%	1%	0%
Cardiovascular, haematological, immunological and respiratory	Codes	0	0	2	0	0	0	0	0	0	0	0	0	0	3
	% BF Domain	0%	0%	2%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	16%
Digestive, metabolic and endocrine	Codes	3	1	10	0	0	1	1	1	1	1	0	1	0	0
	% BF Domain	9%	1%	11%	0%	0%	0%	6%	6%	5%	5%	0%	1%	0%	0%
Genitourinary and reproductive	Codes	3	0	0	0	1	0	0	0	0	0	0	1	0	0
	% BF Domain	9%	0%	0%	0%	33%	0%	0%	0%	0%	0%	0%	1%	0%	0%
Neuromusculoskeletal and movement related	Codes	1	13	11	1	0	123	0	0	0	0	2	3	2	6
	% BF Domain	3%	13%	12%	6%	0%	37%	0%	0%	0%	0%	33%	3%	3%	32%
Skin and related structures	Codes	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	% BF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%
Activities and participation (AP)	Codes	293	520	437	247	74	661	253	196	296	239	36	531	183	57
	%Total ICF	90%	84%	82%	94%	96%	66%	94%	92%	94%	93%	63%	82%	72%	38%
Learning and applying knowledge	Codes	55	188	148	72	5	188	19	19	20	20	6	115	39	4
	% AP Domain	19%	36%	34%	29%	7%	28%	8%	10%	7%	8%	17%	22%	21%	7%
General tasks and demands	Codes	35	3	10	12	4	26	11	11	17	17	7	15	9	1
	% AP Domain	12%	1%	2%	5%	5%	4%	4%	6%	6%	7%	19%	3%	5%	2%
Communication	Codes	42	154	79	60	9	218	10	10	28	28	4	114	38	3
	% AP Domain	14%	30%	18%	24%	12%	33%	4%	5%	9%	12%	11%	21%	21%	5%
Mobility	Codes	34	94	94	48	10	115	107	51	108	52	5	87	24	3
	% AP Domain	12%	18%	22%	19%	14%	17%	42%	26%	36%	22%	14%	16%	13%	5%
Self-care	Codes	52	30	40	23	14	38	67	66	78	77	6	77	32	12
	% AP Domain	18%	6%	9%	9%	19%	6%	26%	34%	26%	32%	17%	15%	17%	21%
Domestic life	Codes	15	0	13	2	4	8	20	20	22	22	1	29	11	7
	% AP Domain	5%	0%	3%	1%	5%	1%	8%	10%	7%	9%	3%	5%	6%	12%
Interpersonal interactions and relationships	Codes	34	39	46	20	8	59	10	10	12	12	3	57	22	4
	% AP Domain	12%	8%	11%	8%	11%	9%	4%	5%	4%	5%	8%	11%	12%	7%
Major life areas	Codes	13	10	2	6	4	5	6	6	8	8	4	27	5	3
	% AP Domain	4%	2%	<1%	2%	5%	1%	2%	3%	3%	3%	11%	5%	3%	5%
Community, social and civic life	Codes	13	2	5	4	16	4	3	3	3	3	0	10	3	20
	% AP Domain	4%	<1%	1%	2%	22%	1%	1%	2%	1%	1%	0%	2%	2%	35%
Environmental factors (EF)	Codes	0	0	0	0	0	0	0	0	0	0	15	0	0	73
	%Total ICF	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	26%	0%	0%	49%
Products and technology	Codes	0	0	0	0	0	0	0	0	0	0	7	0	0	15
	% EF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	47%	0%	0%	21%
Natural environment and human-made changes to environment	Codes	0	0	0	0	0	0	0	0	0	0	0	0	0	17
	% EF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	23%
Support and relationships	Codes	0	0	0	0	0	0	0	0	0	0	5	0	0	12
	% EF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	33%	0%	0%	16%
Attitudes	Codes	0	0	0	0	0	0	0	0	0	0	2	0	0	7
	% EF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	13%	0%	0%	10%
Services, systems and policies	Codes	0	0	0	0	0	0	0	0	0	0	1	0	0	22
	% EF Domain	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	7%	0%	0%	30%

Colour intensity corresponds to higher relative value across component columns (e.g. distribution of ABAS-3’s body function codes), assigned by Microsoft Office conditional formatting. ICF: International Classification of Functioning, Disability and Health; ABAS-3: Adaptive Behaviour Assessment System, 3rd Edition; Bayley-4: Bayley Scales of Infant Development, 4th Edition; DAYC-2: Developmental Assessment of Young Children, 2nd Edition; DP-4: Developmental Profile-4; LIFE-H 3.0: Assessment of Life Habits 3; M-P-R: Merrill Palmer Revised Scales of Development; PEDI-CAT (ASD): Paediatric Evaluation of Disability Inventory, Computer Adaptive Test (Autism Spectrum Disorder Version); MD: Mobility Device; SCOPE: Short Child Occupational Profile; Vineland-3: Vineland Adaptive Behaviour Scales, 3rd Edition; YC-PEM: Young Children’s Participation and Environment Measure; BF: body functions; AP: activities and participation; EF: environmental factors.

Content validity compared to the NDC-related Sets

The content validity of individual measures against the 0–5 years NDC Sets varied (values provided in Table 3). While the median coverage (ranging between 37%–42%) and minimum coverage (19%–28%) of each set was consistent, there was variability across measures of their maximum coverage of the Sets (48%–63%). The coverage of different ICF domains within the Sets reflected a similar distribution as when compared to the full ICF, with measures focussing most frequently on activities and participation items (Mdn = 71%–77%), followed by body functions (Mdn = 17%–44%) with environmental factor codes the least frequently assessed (Mdn = 0%) being covered by only two measures.

Table 3.

Percentage of 0–5 NDC ICF Sets covered by measures.

ICF Core/Code Sets (n)			ABAS-3	Bayley-4	DAYC-2	DP4	LIFE-H 4.0	MPR	PEDI-CAT	PEDI-CAT (MD)	PEDI-CAT (ASD)	PEDI-CAT (ASD, MD)	SCOPE	Vineland-3 Comp	Vinealnd-3 Domain	YC-PEM
eND	Full (113)	Codes	51	55	61	46	24	64	37	34	39	36	40	56	47	35
		%	45%	49%	54%	41%	21%	57%	33%	30%	35%	32%	35%	50%	42%	31%
	BF (37)	Codes	12	19	22	7	2	27	3	3	3	3	5	19	17	5
		%	32%	51%	59%	19%	5%	73%	8%	8%	8%	8%	14%	51%	46%	14%
	AP (46)	Codes	39	36	39	39	22	37	34	31	36	33	23	37	30	8
		%	85%	78%	85%	85%	48%	80%	74%	67%	78%	72%	50%	80%	65%	17%
	EF (30)	Codes	0	0	0	0	0	0	0	0	0	0	12	0	0	22
		%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	40%	0%	0%	73%
ASD 0–5	Full (74)	Codes	37	33	40	32	18	42	37	23	39	25	26	44	37	29
		%	50%	45%	54%	43%	24%	57%	31%	31%	34%	34%	35%	59%	50%	39%
	BF (19)	Codes	11	10	13	5	2	17	3	2	3	2	4	17	15	4
		%	58%	53%	68%	26%	11%	89%	11%	11%	11%	11%	21%	89%	79%	21%
	AP (30)	Codes	26	23	27	27	16	25	34	21	36	23	13	27	22	7
		%	87%	77%	90%	90%	53%	83%	70%	70%	77%	77%	43%	90%	73%	23%
	EF (25)	Codes	0	0	0	0	0	0	0	0	0	0	9	0	0	18
		%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	36%	0%	0%	72%
ADHD 0–5	Full (47)	Codes	24	19	23	18	13	23	15	15	15	15	23	22	20	22
		%	51%	40%	49%	38%	28%	49%	32%	32%	32%	32%	49%	47%	43%	47%
	BF (8)	Codes	7	5	8	2	1	8	2	2	2	2	3	7	6	4
		%	88%	63%	100%	25%	13%	100%	25%	25%	25%	25%	38%	88%	75%	50%
	AP (19)	Codes	17	14	15	16	12	15	13	13	13	13	13	15	14	4
		%	89%	74%	79%	84%	63%	79%	68%	68%	68%	68%	68%	79%	74%	21%
	EF (20)	Codes	0	0	0	0	0	0	0	0	0	0	7	0	0	14
		%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	35%	0%	0%	70%
CP 0–5	Full (29)	Codes	13	13	13	10	7	13	8	7	8	7	14	13	11	12
		%	45%	45%	45%	34%	24%	45%	28%	24%	28%	24%	48%	45%	38%	41%
	BF (8)	Codes	2	3	2	0	1	4	0	0	0	0	1	4	3	2
		%	25%	38%	25%	0%	13%	50%	0%	0%	0%	0%	13%	50%	38%	25%
	AP (11)	Codes	11	10	11	10	6	9	8	7	8	7	7	9	8	3
		%	100%	91%	100%	91%	55%	82%	73%	64%	73%	64%	64%	82%	73%	27%
	EF (10)	Codes	0	0	0	0	0	0	0	0	0	0	6	0	0	7
		%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	60%	0%	0%	70%
EDD	Full (72)	Codes	33	39	44	29	14	45	23	20	25	22	25	34	28	20
		%	46%	54%	61%	40%	19%	63%	32%	28%	35%	31%	35%	47%	39%	28%
	BF (29)	Codes	8	15	18	5	1	20	2	2	2	2	5	12	11	4
		%	28%	52%	62%	17%	3%	69%	7%	7%	7%	7%	17%	41%	38%	14%
	AP (28)	Codes	25	24	26	24	13	25	21	18	23	20	15	22	17	4
		%	89%	86%	93%	86%	46%	89%	75%	64%	82%	71%	54%	79%	61%	14%
	EF (15)	Codes	0	0	0	0	0	0	0	0	0	0	5	0	0	12
		%	0%	0%	0%	0%	0%	0%	0%	0%	0%	0%	33%	0%	0%	80%

Colour intensity corresponds to larger value across rows, assigned by Microsoft office conditional formatting. NDC: neurodevelopmental condition; ABAS-3: Adaptive Behaviour Assessment System, 3rd Edition; Bayley-4: Bayley Scales of Infant Development, 4th Edition; DAYC-2: Developmental Assessment of Young Children, 2nd Edition; DP-4: Developmental Profile-4; LIFE-H 3.0: Assessment of Life Habits 3; M-P-R: Merrill Palmer Revised Scales of Development; PEDI-CAT (ASD): Paediatric Evaluation of Disability Inventory, Computer Adaptive Test (Autism Spectrum Disorder Version); MD: Mobility Device; SCOPE: Short Child Occupational Profile; Vineland-3: Vineland Adaptive Behaviour Scales, 3rd Edition; YC-PEM: Young Children’s Participation and Environment Measure; eND: Early Neurodevelopmental; BF: Body Functions; AP: Activities and Participation; EF: Environmental Factors; ASD: Autism Spectrum Disorder; ADHD: Attention Deficit/Hyperactivity Disorder; CP: cerebral palsy; EDD: Early Delay and Disability.

Based on medians, the measures that had the best content validity across the four full individual NDC Sets were the MPR (Mdn = 57%), DAYC-2 (Mdn = 54%), and Vineland-3 Comprehensive (Mdn = 47%). These measures also covered the most body function codes across the Sets. However, the DAYC-2, ABAS-3 and DP4 had the best median coverage of the Sets’ activity and participation codes (Mdn = 90%; 89% and 86%, respectively). When considering environmental factors, the YC-PEM consistently covered more codes (Mdn = 72%) than the SCOPE (Mdn = 36%).

Linking revealed that none of the identified measures consistently and comprehensively assessed codes across the individual Sets, with the MPR most comprehensively covering the eND and EDD Sets, the Vineland-3 Comprehensive the ASD and CP Sets and ABAS-3 the ADHD Set. The MPR was the, or one of the, most comprehensive measures for all five of the Sets’ body function codes, joined by the Vineland-3 Comprehensive for the ASD and CP Sets, and the DAYC-2 for the ADHD Set. When considering only the activity and participation codes, multiple measures covered the highest percentage of most Sets. The ABAS-3, DAYC-2 and DP4 were most comprehensive for the eND Set, and the DAYC-2, DP4 and Vineland-3 Comprehensive covering the most activity and participation codes of the ASD Set. Only the ABAS-3 was the most comprehensive of the ADHD Set, while it and the DAYC-2 were both the most comprehensive of the CP Set’s activity and participation domain. Finally, the DAYC-2 was the most comprehensive in covering the EDD Set. Considering the environmental factor domains of the Sets, the YC-PEM was consistently the most comprehensive.

The percentage of each Set each measure covered, including a breakdown by the domains of the ICF, is outlined in Table 3. In addition to the inclusion of all relevant concepts, content validity includes the exclusion of irrelevant concepts, so the percentages of codes assigned to each measure that were relevant to the Sets are outlined in Supplementary file 5.

Discussion

Content validity is a foundational property of assessment measures, which should be considered alongside other psychometric properties when determining the suitability of a measure for a specific purpose (Mokkink et al., 2010). Despite the important role that measures of functioning play in the clinical pathway of young children with NDCs (Commonwealth of Australia, 2013; Miltenberger et al., 2016; Msall, 2005; National Institute for Health and Care Excellence, 2011; Whitehouse et al., 2018), the content validity of these measures is rarely established (McConachie et al., 2015; Price et al., 2018). The ICF and associated Code and Code Sets provide a gold standard against which to assess the content validity of common measures of functioning for young children with NDCs.

The linking results demonstrated that the measures reviewed had varying levels of content validity when compared to the standard of the Sets. None of the measures were comprehensive enough to cover all concepts across the three domains of the ICF, with even the most comprehensive measures covering less than 60% of the eND Set codes. When considering only the body function codes or activities and participation codes within the Set, different measures were better suited to assessing different domains. Even measures designed to assess the same concept (e.g. the Vineland-3 and ABAS-3 both assess adaptive behaviour) differed in regard to their focus on body functions and activities and participation (Harrison & Oakland, 2015; Sparrow, Cicchetti, & Saulnier, 2016). Identified measures most comprehensively assessed the activities and participation domain of the Sets. Given that the performance of activities and participation is the ‘result’ of functioning and is readily observed, this finding is not surprising (Law et al., 2017; WHO, 2007).

While only two of the measures of functioning included in the present review directly assessed environmental factors, several measures assessed a child’s level of functioning alongside the presence of supports (e.g. LIFE-H) or through explicit instructions to rate the child’s functional performance with (e.g. PEDI-CAT) or without (e.g. ABAS-3 and Vineland-3) supports. The ICF and other models related to functioning emphasise the interplay between an individual’s skills, abilities or body functions, the activities and tasks they participate in, and their environment on their overall functioning (Townsend & Polatajko, 2013; WHO, 2007). The ICF in particular also includes personal factors as a contextual component alongside environmental factors as an influence on functioning (WHO, 2007). Adaptive behaviour, although less focused on the interplay between these components, is still generally accepted to be defined as ‘the skills an individual requires . . . to be able to cope with the social and natural demands in their environment’ (Price et al., 2018, p. 1). Without assessing if specific environmental supports and barriers are present and how they interact with and individual’s skills and activities, providing an accurate and holistic picture of an individual’s abilities and support needs is not possible (Anaby et al., 2013; Turpin & Iwama, 2010; WHO, 2007). An assessment of functioning without considering the environment or personal factors can impact funding and intervention when assessments are used in clinical practice and may lead to misinterpretation of results when used as outcome measures in research studies.

The co-occurrence of NDCs is common, and young children may go on to receive a diagnosis not identified at the time of the initial assessment (Licari et al., 2019; Miodovnik et al., 2015). Furthermore, NDCs can have a diverse impact on functioning not captured in diagnostic criteria (Andrews et al., 2015; Flynn & Healy, 2012; Licari et al., 2020). In the absence of a new assessment with greater content validity in comparison to the eND Set, a transdisciplinary approach using a battery of measures alongside clinical reasoning is recommended. While the use of a battery or comprehensive tool in assessing the functioning of young children with NDCs adds to the resources required to assess functioning, this approach likely provides a more valid and holistic picture of a child’s functioning, and more reliably informing intervention planning. Therefore, sufficient funding should be made available to facilitate the use of appropriate measures in order to avoid inaccurate or inappropriate findings and associated funding or therapeutic consequences (Whitehouse et al., 2018). When a full assessment is not required, careful consideration of the purpose of the assessment and needed information should be used to determine appropriate measures through clinical reasoning (Kramer et al., 2009). The selection of appropriate assessments to include in a battery, or for more specific assessment purposes can be aided by the linking results presented.

When selecting assessments, their original design should also be considered, including their perspectives, response options and whether they are designed to be a broad comprehensive assessment (Cieza et al., 2019; Law et al., 2017). For example, the PEDI-CAT and PEDI-CAT (ASD) are not designed to capture a specific and detailed picture of functioning. Instead they use item response theory and high internal consistency to estimate a child’s broader level of functioning based on a relatively small number of question responses (Haley et al., 2020). Also to be considered is the difference between functional performance and capacity, and whether functioning is rated with or without supports (WHO, 2007). The difference between these two approaches is especially relevant when using these assessments to determine support needs, a base level of functioning or to measure change resulting from an intervention. Lack of clarity between functioning with and without supports could impact comparisons to norms and potentially influence the appropriateness of funding and supports available to the child.

Limitations

A proportion of items from the measures were not able to be linked to defined ICF codes. The other specified codes were used to account for limitations of the ICF regarding the early stages of infant and toddler development and changes in activities of daily life over time (e.g. use of technology). While updates to the ICF have been made, most notably the addition of items relevant to children and youth, this finding supports the continued revision of the ICF and Sets to maintain their applicability over time, and for more specific populations, such as infants (WHO, 2007). However, this use of other specified codes did not significantly influence the results of this study, as they were usually converted to their parent second-level code before comparison to the Sets.

Due to the Sets consisting of second-level codes, this research only evaluates the measures’ content validity at this level of detail. Many ICF codes have third, and sometimes fourth level, subcodes which provide greater levels of detail. In the present review, if measures assessed these third and fourth level codes, they were collapsed into their parent second-level code before comparison to the Sets. Therefore, while a measure may have assessed a second-level code, this is not necessarily an indication that the measure assesses all the details within that code.

While this study assessed the content validity of measures’ full item banks against the ICF and Sets as a gold standard, this will not always be a true reflection of the measures’ content validity in clinical practice. The CAT design of the PEDI-CAT versions, and the basal and ceiling administration methods of other measures (including the MPR, Vineland-3, and DAYC-2), influence how many, and which, questions are scored (Haley et al., 2020; Roid & Sampers, 2004; Sparrow, Saulnier, et al., 2016; Voress et al., 2012). Therefore, areas of the ICF and Sets covered by the full item bank will not always be assessed for an individual. Given this, results presented in this article will generally be an overestimation of the content validity of the measures. Further work to assess the content validity of the measures, as administered to a representative sample of young children with NDCs, is needed, and is currently being undertaken for the PEDI-CAT (ASD).

Clinical significance

The results of this review can be used by clinicians, researchers and policymakers when selecting assessments to assess the functioning of young children with NDCs, or as outcome measures in research. While an important consideration, content validity is not the sole indicator in determining the appropriateness of assessments. The whole range of psychometric properties and clinical utility should be evaluated before using an assessment (Mokkink et al., 2010; Smart, 2006). In addition, the context in which the assessment is being utilised will determine the assessment properties best suited to practice and how appropriate the ICF Sets are as a gold standard for content validity (Mokkink et al., 2010).

Supplemental Material

sj-docx-1-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental material, sj-docx-1-aut-10.1177_13623613211036809 for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions by Emily D’Arcy, Kerry Wallace, Angela Chamberlain, Kiah Evans, Benjamin Milbourn, Sven Bölte, Andrew JO Whitehouse and Sonya Girdler in Autism

Supplemental Material

sj-docx-2-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental material, sj-docx-2-aut-10.1177_13623613211036809 for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions by Emily D’Arcy, Kerry Wallace, Angela Chamberlain, Kiah Evans, Benjamin Milbourn, Sven Bölte, Andrew JO Whitehouse and Sonya Girdler in Autism

Supplemental Material

sj-docx-3-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental material, sj-docx-3-aut-10.1177_13623613211036809 for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions by Emily D’Arcy, Kerry Wallace, Angela Chamberlain, Kiah Evans, Benjamin Milbourn, Sven Bölte, Andrew JO Whitehouse and Sonya Girdler in Autism

Supplemental Material

sj-docx-4-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental material, sj-docx-4-aut-10.1177_13623613211036809 for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions by Emily D’Arcy, Kerry Wallace, Angela Chamberlain, Kiah Evans, Benjamin Milbourn, Sven Bölte, Andrew JO Whitehouse and Sonya Girdler in Autism

Supplemental Material

sj-docx-5-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental material, sj-docx-5-aut-10.1177_13623613211036809 for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions by Emily D’Arcy, Kerry Wallace, Angela Chamberlain, Kiah Evans, Benjamin Milbourn, Sven Bölte, Andrew JO Whitehouse and Sonya Girdler in Autism

Footnotes

Acknowledgements

The authors would like to thank the measures’ publishers for their permission and support in obtaining assessments for analysis. This research will be used towards obtaining a Doctor of Philosophy (Occupational Therapy) by the first author.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the financial support of the Cooperative Research Centre for Living with Autism (Autism CRC), established and supported under Australian Government’s Cooperative Research Centres Programme. A.J.O.W. is supported by an Investigator Grant from the National Health and Medical Research Council (grant no. 1173896).

ORCID iDs

Emily D’Arcy

Kiah Evans

Sven Bölte

Supplemental material

Supplemental material for this article is available online.

References

Alpern

(2020). Developmental profile 4. Western Psychological Services.

Alvares

G. A.

Bebbington

Cleary

Evans

Glasson

E. J.

Maybery

M. T.

Pillar

Uljarević

Varcin

Wray

Whitehouse

A. J. O.

(2020). The misnomer of ‘high functioning autism’: Intelligence is an imprecise predictor of functional abilities at diagnosis. Autism, 24(1), 221–232. https://doi.org/10.1177/1362361319852831

Anaby

Hand

Bradley

DiRezze

Forhan

DiGiacomo

Law

(2013). The effect of the environment on participation of children and youth with disabilities: A scoping review. Disability and Rehabilitation, 35(19), 1589–1598. https://doi.org/10.3109/09638288.2012.748840

Andrews

Falkmer

Girdler

(2015). Community participation interventions for children and adolescents with a neurodevelopmental intellectual disability: A systematic review. Disability and Rehabilitation, 37(10), 825–833. https://doi.org/10.3109/09638288.2014.944625

Asher

I. E.

(Ed.). (2014). Asher’s occupational therapy assessment tools: An annotated index (4th ed.). American Occupational Therapy Association.

Ashwood

K. L.

Tye

Azadi

Cartwright

Asherson

Bolton

(2015). Brief report: Adaptive functioning in children with ASD, ADHD and ASD + ADHD. Journal of Autism and Developmental Disorders, 45(7), 2235–2242. https://doi.org/10.1007/s10803-014-2352-y

Bayley

Aylward

(2020). Bayley Scales of Infant Development (4th ed.). Pearson.

Bölte

de Schipper

Holtmann

Karande

de Vries

P. J.

Selb

Tannock

(2014). Development of ICF Core Sets to standardize assessment of functioning and impairment in ADHD: The path ahead. European Child & Adolescent Psychiatry, 23(12), 1139–1148. https://doi.org/http://dx.doi.org/10.1007/s00787-013-0496-5

Bölte

Mahdi

Coghill

Gau

S. S.-F.

Granlund

Holtmann

Karande

Levy

Rohde

L. A.

Segerer

de Vries

P. J.

Selb

(2018). Standardised assessment of functioning in ADHD: Consensus on the ICF Core Sets for ADHD. European Child & Adolescent Psychiatry, 27(10), 1261–1281. https://doi.org/10.1007/s00787-018-1119-y

10.

Bölte

Mahdi

de Vries

P. J.

Granlund

Robison

J. E.

Shulman

Swedo

Tonge

Wong

Zwaigenbaum

Segerer

Selb

(2019). The Gestalt of functioning in autism spectrum disorder: Results of the international conference to develop final consensus International Classification of Functioning, Disability and Health core sets. Autism, 23(2), 449–467. https://doi.org/10.1177/1362361318755522

11.

Bowyer

Kramer

Polszaj

Ross

Schwartz

Keilhofner

Kramer

(2008). Short Child Occupational Profile (Version 2.2). MOHO Clearinghouse.

12.

Cieza

Fayed

Bickenbach

Prodinger

(2019). Refinements of the ICF linking rules to strengthen their potential for establishing comparability of health information. Disability and Rehabilitation, 41(5), 574–583. https://doi.org/10.3109/09638288.2016.1145258

13.

Commonwealth of Australia. (2013). National Disability Insurance Scheme Act 2013. https://www.legislation.gov.au/Details/C2013A00020

14.

Flynn

Healy

(2012). A review of treatments for deficits in social skills and self-help skills in autism spectrum disorder. Research in Autism Spectrum Disorders, 6(1), 431–441. https://doi.org/10.1016/j.rasd.2011.06.016

15.

Haley

Coster

Dumas

Fragala-Pinkham

Moed

Kramer

Kao

Y.-C.

Feng

Ludlow

(2020). Pediatric Evaluation of Disabilty Inventory Computer Adaptive Test. CRE Care. https://www.pedicat.com/

16.

Haley

Coster

Dumas

Fragala-Pinkham

Moed

Kramer

J. M.

Feng

Y.-C.

Ludlow

(2019). PEDI-CAT Version 1.4.3 development, standardization and administration manual. Boston University.

17.

Harrison

Oakland

(2015). Adaptive behavior assessment system (3rd ed.). Western Psychological Services.

18.

International Network on the Disability Creation Process. (2005). Assessment of life habits.

19.

Khetani

Coster

Law

Bedell

(2013). Young children’s participation and environment measure. CanChild.

20.

Kielhofner

(2006). Developing and evaluating quantitative data collection instruments. In Kielhofner

(Ed.), Research in occupational therapy: Methods of inquiry for enhancing practice (1st ed., pp. 155–176). F. A. Davis Company.

21.

Kramer

Bowyer

O’Brien

Kielhofner

Maziero-Barbosa

(2009). How interdisciplinary pediatric practitioners choose assessments. Canadian Journal of Occupational Therapy/Revue Canadienne d’ergotherapie, 76(1), 56–64. https://doi.org/10.1177/000841740907600114

22.

Law

Baptiste

Carswell

McColl

Polatajko

Pollack

(2005). Canadian Occupational Performance Measure (COPM) (4th ed.). Canadian Association of Occupational Therapists.

23.

Law

Baum

Dunn

(2017). Measuring occupational performance: Supporting best practice in occupational therapy (3rd ed.). Slack Incorporated.

24.

Leonardi

Sykes

C. R.

Cawley

Hollenweger

Snyman

Madden

R. H.

Kraus

Camargo

Raggi

Van Gool

C. H.

Martinuzzi

(2015). Disability and rehabilitation do we really need to open a classification box on personal factors in ICF? Disability and Rehabilitation, 38(13), 1327–1328. https://doi.org/10.3109/09638288.2015.1089604

25.

Licari

Alvares

G. A.

Varcin

Evans

K. L.

Cleary

Reid

S. L.

Glasson

E. J.

Bebbington

Reynolds

Wray

Whitehouse

A. O. J.

(2019). Prevalence of motor difficulties in autism spectrum disorder: Analysis of a population-based cohort. Autism Research, 13(2), 298–306. https://doi.org/10.1002/aur.2230

26.

Licari

Williams

, & Impact for DCD Team. (2020). National survey evaluating the impact of developmental coordination disorder in Australia: Summary of results. Telethon Kids Institute. https://www.vu.edu.au/sites/default/files/impact-for-dcd-report.pdf

27.

Madden

R. H.

Bundy

(2019). The ICF has made a difference to functioning and disability measurement and statistics. Disability and Rehabilitation, 41(12), 1450–1462. https://doi.org/10.1080/09638288.2018.1431812

28.

Mahdi

Marisa

Tamara

Melissa

Nidhi

Omar

Mats

G. J.

V. P.

Lonnie

Sven

(2018). An international qualitative study of functioning in autism spectrum disorder using the World Health Organization international classification of functioning, disability and health framework. Autism Research, 11(3), 463–475. https://doi.org/doi:10.1002/aur.1905

29.

Majnemer

(Ed.). (2012). Measures for children with developmental disabilities: An ICF-CY approach. Mac Keith Press.

30.

Maritz

Baptiste

Darzins

S. W.

Magasi

Weleschuk

Prodinger

(2018). Linking occupational therapy models and assessments to the ICF to enable standardized documentation of functioning. Canadian Journal of Occupational Therapy, 85(4), 330–341. https://doi.org/10.1177/0008417418797146

31.

McConachie

Parr

J. R.

Glod

Hanratty

Livingstone

Oono

I. P.

Robalino

Baird

Beresford

Charman

Garland

Green

Gringras

Jones

Law

Le Couteur

A. S.

Macdonald

McColl

E. M.

Morris

… Williams

(2015). Systematic review of tools to measure outcomes for young children with autism spectrum disorder. Health Technology Assessment, 19(41), 1-506. https://doi.org/10.3310/hta19410

32.

Miltenberger

R. G.

Bloom

S. E.

Sanchez

Valbuena

D. A.

(2016). Functional assessment. In Singh

N. N.

(Ed.), Handbook of evidence-based practices in intellectual and developmental disabilities, evidence-based practices in behavioral health (pp. 69–95). Springer. https://doi.org/10.1007/978-3-319-26583-4_4

33.

Miodovnik

Harstad

Sideridis

Huntington

(2015). Timing of the diagnosis of attention-deficit/hyperactivity disorder and autism spectrum disorder. Pediatrics, 136(4), e830–e837. https://doi.org/10.1542/peds.2015-1502

34.

Mokkink

L. B.

Terwee

C. B.

Patrick

D. L.

Alonso

Stratford

P. W.

Knol

D. L.

Bouter

L. M.

de Vet

H. C. W.

(2010). The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. Journal of Clinical Epidemiology, 63(7), 737–745. https://doi.org/10.1016/j.jclinepi.2010.02.006

35.

Msall

M. E.

(2005). Measuring functional skills in preschool children at risk for neurodevelopmental disabilities. Mental Retardation and Developmental Disabilities Research Reviews, 11(3), 263–273. https://doi.org/10.1002/mrdd.20073

36.

Mulligan

(Ed.). (2014). Occupational therapy evaluation for children: A pocket guide (2nd ed.). Lippincott Williams & Wilkins.

37.

National Institute for Health and Care Excellence. (2011). Autism spectrum disorder in under 19s: Recognition, referral and diagnosis. https://www.nice.org.uk/guidance/cg128

38.

Pan

Y.-L.

Hwang

A.-W.

Simeonsson

R. J.

Liao

H.-F.

(2015). ICF-CY code set for infants with early delay and disabilities (EDD Code Set) for interdisciplinary assessment: A global experts survey. Disability and Rehabilitation, 37(12), 1044–1054. https://doi.org/10.3109/09638288.2014.952454

39.

Price

J. A.

Morris

Z. A.

Costello

(2018). The application of adaptive behaviour models: A systematic review. Behavioral Sciences, 8(1), 11. https://doi.org/10.3390/bs8010011

40.

Riou

Ghosh

Francoeur

Shevell

(2009). Global developmental delay and its relationship to cognitive skills. Developmental Medicine & Child Neurology, 51(8), 600–606. https://search-proquest-com.dbgw.lis.curtin.edu.au/docview/195611888/fulltextPDF/C037A26B6F8D4DACPQ/1?accountid=10382

41.

Roid

Sampers

(2004). Merrill-Palmer-revised scales of development. Stoelting.

42.

Schiariti

Selb

Cieza

O’Donnell

(2015). International Classification of Functioning, Disability and Health Core Sets for children and youth with cerebral palsy: A consensus meeting. Developmental Medicine & Child Neurology, 57(2), 149–158. https://doi.org/10.1111/dmcn.12551

43.

Selb

Escorpizo

Kostanjsek

Stucki

Üstün

Cieza

(2015). A guide on how to develop an International Classification of Functioning, Disability and Health Core Set. European Journal of Physical and Rehabilitation Medicine, 51(1), 105–117.

44.

Smart

(2006). A multi-dimensional model of clinical utility. International Journal for Quality in Health Care, 18(5), 377–382. https://doi.org/10.1093/intqhc/mzl034

45.

Sparrow

Cicchetti

Saulnier

(2016). Vineland Adaptive Behavior Scales (3rd ed.). Pearson Assessments.

46.

Sparrow

Saulnier

Cicchetti

Doll

(2016). Vineland-3: Vineland adaptive behavior scales. Manual. Pearson Assessments.

47.

Spittle

Orton

Anderson

Boyd

Doyle

L. W.

(2012). Early developmental intervention programmes post-hospital discharge to prevent motor and cognitive impairments in preterm infants. Cochrane Database of Systematic Reviews, 12, CD005495. https://doi.org/10.1002/14651858.cd005495.pub3

48.

Townsend

Polatajko

(2013). Enabling occupation II : Advancing an occupational therapy vision for health, well-being, and justice through occupation (2nd ed.). Canadian Association of Occupational Therapists.

49.

Turpin

M. J.

Iwama

M. K.

(2010). Using occupational therapy modes in practice: A fieldguide. Elsevier Health Sciences APAC.

50.

Voress

Maddox

Hammill

(2012). Developmental assessment of young children (2nd ed.). Pro-Ed.

51.

Whitehouse

Evans

Eapen

Wray

(2018). A national guideline for the assessment and diagnosis of autism spectrum disorders in Australia. Cooperative Research Centre for Living with Autism.

52.

Whitehouse

Varcin

Waddington

Sulek

Bent

Ashburner

Eapen

Goodall

Hudry

Roberts

Silove

Trembath

(2020). Interventions for children on the autism spectrum: A synthesis of research evidence. Cooperative Research Centre for Living with Autism.

53.

World Health Organization. (2007). International classification of functioning, disability and health. Version for children and youth.

54.

World Health Organization. (2012). International Classification of Functioning, Disability and Health (ICF). https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health

55.

Zander

Bölte

(2015). The new DSM-5 impairment criterion: A challenge to early autism spectrum disorder diagnosis? Journal of Autism, 45, 3634–3643. https://doi.org/10.1007/s10803-015-2512-8

Supplementary Material

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Content validation of common measures of functioning for young children against the International Classification of Functioning,Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Abstract

Lay abstract

Keywords

Introduction

Methods

Grey literature search and comparison to ICF chapters

Full linking and comparison to ICF and ICF Sets

Community involvement

Results

Full linking and comparison to ICF and eND, ASD, CP and EDD Sets

Perspectives of measures

Content validity compared to the ICF domains and chapters

Content validity compared to the NDC-related Sets

Discussion

Limitations

Clinical significance

Supplemental Material

sj-docx-1-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental Material

sj-docx-2-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental Material

sj-docx-3-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental Material

sj-docx-4-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Supplemental Material

sj-docx-5-aut-10.1177_13623613211036809 – Supplemental material for Content validation of common measures of functioning for young children against the International Classification of Functioning, Disability and Health and Code and Core Sets relevant to neurodevelopmental conditions

Footnotes

Acknowledgements

Funding

ORCID iDs

Supplemental material

References

Supplementary Material