Content development of the Child Community Health Inclusion Index: An evaluation tool for measuring inclusion of children with disabilities in the community
Bibliographic record
Abstract
BACKGROUND: Addressing barriers in the environment can contribute to health and quality of life for children with disabilities and their families. The Community Health Inclusion Index (CHII) is a measurement tool developed in the United States to identify environmental barriers and facilitators to community health inclusion. The CHII adopts an adult viewpoint and aspects crucial for children may have been omitted. AIMS: This study aimed to develop a comprehensive list of items that are relevant for the community inclusion of children with disabilities in the Canadian context. METHODS: The relevance and priority of items generated from a review of existing guidelines and best practice recommendations for community inclusion were rated as a dichotomous response and discussed by an expert panel in relevant fields related to children with disabilities. RESULTS: A total of 189 items from 12 instruments and best practice guidelines were identified. Expert consensus contributed to a relevant and comprehensive list of items. Expert suggestions were considered to refine and reduce the item list. CONCLUSION: This study highlights the importance of a child version of a community inclusion tool, as the needs of children with disabilities differ from those of adults. It can help communities improve inclusion of children with disabilities and inform health promotion initiatives for this population.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".