Application of the child community health inclusion index for measuring health inclusion of children with disabilities in the community: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Participation in the community is a fundamental human right for children with disabilities and is a key component of their health and development. Inclusive communities can enable children with disabilities to participate fully and effectively. The Child Community Health Inclusion Index (CHILD-CHII) is a comprehensive assessment tool developed to examine the extent to which community environments foster healthy, active living for children with disabilities. OBJECTIVES: To assess the feasibility of applying the CHILD-CHII measurement tool across different community settings. METHODS: Participants recruited through maximal representation, and purposeful sampling from four community sectors (Health, Education, Public Spaces, Community Organizations) applied the tool on their affiliated community facility. Feasibility was examined by assessing length, difficulty, clarity, and value for measuring inclusion; each rated on a 5-point Likert scale. Participants provided comments for each indicator through the questionnaire and a follow-up interview. RESULTS: Of the 12 participants, 92% indicated that the tool was 'long' or 'much too long'; 66% indicated that the tool was clear; 58% indicated that the tool was 'valuable' or 'very valuable'. No clear consensus was obtained for the level of difficulty. Participants provided comments for each indicator. CONCLUSION: Although the length of the tool was regarded as long, it was seen to be comprehensive and valuable for stakeholders in addressing the inclusion of children with disabilities in the community. The perceived value and the evaluators' knowledge, familiarity, and access to information can facilitate use of the CHILD-CHII. Further refinement and psychometric testing will be conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".