Developing a non-categorical measure of child health using administrative data.
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the potential of linked administrative data for research on child health. This analysis describes the application of a non-categorical survey-based tool, the Children with Special Health Care Needs (CSHCN) Screener, to administrative data. DATA AND METHODS: Five Screener items were applied to linked administrative health data from Population Data British Columbia. Hospital admissions and demographic and community characteristics for a cohort of children aged 6 to 10 in 2006 were examined to validate the use of these items. RESULTS: Overall, 17.5% of children were identified as CSHCN. An estimated 14% of children used more medical care and 5.2% had more functional limitations than is usual for children of the same age; 3.3% were prescribed long-term medication; 1.9% needed/received treatment or counselling; and 0.1% needed/received special therapy. Boys were more likely than girls to be identified as CSHCN. INTERPRETATION: With some limitations, the CSHCN Screener can be applied to Canadian administrative health data.
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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 | 0.029 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".