Preventive screening. What factors influence testing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 it