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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".