Physician-related determinants of cervical cancer screening among Caribbean women in Toronto.
Bibliographic record
Abstract
BACKGROUND: Minority women in Canada are less likely to be screened for cervical cancer than their counterparts in the general population, despite the fact that the proportion of these women who consult a general practitioner about their health each year is similar to minority women. This study examined the physician and practice characteristics associated with Pap testing and perceived barriers to Pap testing of family physicians serving the Caribbean community of Toronto. METHODS: A mail-back questionnaire was sent to Toronto family physicians practicing in neighborhoods with a high proportion of Caribbean Canadians. RESULTS: Although 79.7% of the 64 participating physicians reported that they were 'very likely" to include Pap testing during an annual check-up, nearly half did not believe that the majority of Caribbean patients were actually screened. The amount of time a physician spent on patient education was significantly associated with his/her likelihood of screening. Male physicians who reported a high proportion of Caribbean female patients in their practices were significantly less likely to screen for cervical cancer than those who saw fewer Caribbean patients. CONCLUSION: These findings suggest that an increased emphasis on patient education is important to increase screening practice and that physician gender may be 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".