Bibliographic record
Abstract
OBJECTIVES: This article documents the extent of proxy reporting in Statistics Canada's National Population Health Survey (NPHS) and explores associations between reporting status and the prevalence and incidence of selected health problems. DATA SOURCES: Data are from the household cross-sectional (1994/95, 1996/97 and 1998/99) and longitudinal (1994/95 to 2000/01) components of the NPHS. Supplemental data are from the 2000/01 Canadian Community Health Survey. ANALYTICAL TECHNIQUES: Estimates of health conditions from the two cross-sectional files that are produced for each NPHS cycle were compared. The file with the lower proxy reporting rate was expected to yield higher prevalence rates. Multivariate analyses of the longitudinal data were used to examine associations between changes in reporting status and the incidence of the selected conditions. MAIN RESULTS: Compared with the 1998/89 General file, in which proxy reporting was more common, the 1998/99 Health file yields higher estimates of certain health conditions. Declines in proxy reporting rates over time are generally associated with greater increases in estimates. Analyses based on the longitudinal file suggest that the incidence of some conditions may also be subject to a proxy effect.
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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 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".