Measuring chronic health condition and disability as distinct concepts in national surveys of school-aged children in Canada: a comprehensive review with recommendations based on the ICD-10 and ICF
Bibliographic record
Abstract
PURPOSE: With the aim of improving the measurement of child health and disability in survey research, this paper reviews the coverage of chronic health conditions and the domains of disability and related environmental factors as they are laid out in the ICD-10 and ICF, respectively, in national surveys of school-aged children conducted in Canada since 1980. Recommendations are made for future survey use and construction. METHODS: Two reviewers independently examined each of the surveys. Coverage of chronic health conditions, the domains of disability, and environmental factors in survey questions was identified by mapping question content onto ICD-10 and ICF codes. The reviewers then compared their findings and came to a final consensus. RESULTS: Surveys vary in the range and depth of coverage of the ICD-10 and ICF chapters. Disability surveys and health surveys for persons aged 12 and over contain the most comprehensive lists of chronic conditions. Coverage of impairments is limited. Coverage of activity limitations and participation restrictions is most limited in the domains of personal care and domestic life. Environmental factors not covered include natural environmental changes, attitudes, and policies. CONCLUSIONS: Development of a comprehensive standard list of chronic health conditions based on the ICD-10 and development of standard survey measures of the domains of disability and environmental factors based on the ICF for use in surveys of school-aged children would facilitate an understanding of children's health and disability in the context of the current international health framework provided by the World Health Organization.
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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 | 0.054 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.047 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".