Content validation of the child community health inclusion index: a modified e-Delphi study
Bibliographic record
Abstract
Participation is a human right and a key component of health and development in children with disabilities. Inclusive communities and environments facilitate participation. Currently, there are no measures that comprehensively assess the inclusion of children with disabilities in Canadian communities. This study aims to establish the content validity and improve the clarity of the Child Community Health Inclusion Index (CHILD-CHII). A modified e-Delphi technique was conducted. A purposeful convenience sample of community stakeholders was recruited. The importance and clarity of each item were rated on a Likert scale in two separate rounds. Depending on consensus, items were retained, modified, or omitted. A total of 48 participants completed the first round of the Delphi technique and 38 completed the second. In total, 106 items were presented of which 101 items were rated important with high consensus and were retained. A total of 17 items were modified for clarity and presented in the second round. In the second round, all 17 modified items were deemed clearer. The CHILD-CHII and its validated content assess aspects of the community that align with social determinants of health. Measuring these aspects may identify barriers to inclusion and inform the development of interventions, health strategies, and policies to improve community inclusion and child health.
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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 | 0.160 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".