Bibliographic record
Abstract
OBJECTIVE: To determine factors associated with having preventive screening tests in a population-based sample of Ontario women. DESIGN: Secondary analysis of data from Statistics Canada's National Population Health Survey linked to data from the Ontario Health Insurance Plan to ascertain whether women aged 20 or older had Pap smears, mammography, bone densitometry, or cholesterol testing. Factors associated with having testing were subjected to logistic regression analysis. SETTING: Ontario. PARTICIPANTS: Women aged 20 or older; from 19,600 Canadian households, 2232 Ontario women gave consent to linkage of administrative databases. MAIN OUTCOME MEASURES: Age-specific population screening rates. Odds ratios and probabilities of having screening in relation to socioeconomic, geographic, and physician-associated factors. RESULTS: Having screening was associated with age, income, education, and place of residence. Women with regular physicians were more likely to have Pap smears (odds ratio [OR] 4.4, range 1.7 to 12), densitometry (OR 22, range 3.6 to 140), and cholesterol testing (OR 8.0, range 2.3 to 29). Women who had periodic health examinations were more likely to have Pap smears (OR 6.7, range 4.6 to 9.8), mammograms (OR 3.7, range 2.3 to 5.9), densitometry (OR 3.7, range 1.3 to 10.5), and cholesterol testing (OR 3.0, range 2.0 to 4.5). The probability of having testing increased with number of visits a year to a doctor, but ceased to increase after three visits. CONCLUSION: Having screening tests was associated with socioeconomic factors including income, education, and place of residence. Patients who went to doctors for episodic care only were less likely to have preventive screening than patients who went for periodic health examinations.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".