The role of physicians in mammography referral for older Caribbean women in Canada.
Bibliographic record
Abstract
BACKGROUND: Despite the fact that the proportion of immigrant and minority women who consult a general practitioner about their health is similar to that of their Canadian-born counterparts, studies suggest that they are less likely to be screened for breast cancer. This study examines physician characteristics associated with mammography referral and perceived barriers to mammography among family physicians serving the Caribbean community of Toronto. METHODS: The study consisted of a mail-back family physician survey. RESULTS: Among the 64 physicians who responded to the survey, over half reported that they were "very likely" to refer women for mammography during a regular preventive check-up. Among physician variables, only the amount of time spent on patient education was significantly associated with the likelihood of referral. Regarding perceived barriers, for male physicians, patient refusal and intervention causing patient discomfort were significantly associated with referral. For female physicians, only forgetting to provide service was identified as a significant barrier to referral. INTERPRETATION: An increased emphasis on patient education may help to increase screening referral among all physicians. Gender differences in perceived barriers to referral suggest that the gender of the physician is of major importance to the Caribbean community.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".