Breast cancer screening. First Nations communities in New Brunswick.
Bibliographic record
Abstract
OBJECTIVE: To determine use of breast cancer screening and barriers to screening among women in First Nations communities (FNCs). DESIGN: Structured, administered survey. SETTING: Five FNCs in New Brunswick. PARTICIPANTS: One hundred thirty-three (96%) of 138 eligible women between the ages of 50 and 69 years. INTERVENTIONS: After project objectives, methods, and expected outcomes were discussed with community health representatives, we administered a 32-item questionnaire on many aspects of breast cancer screening. MAIN OUTCOME MEASURES: Rate of use of mammography and other breast cancer screening methods, and barriers to screening. RESULTS: Some 65% of participants had had mammography screening within the previous 2 years. Having mammography at recommended intervals and clinical breast examinations (CBEs) yearly were significantly associated with having had a physician recommend the procedures (P < .001). A family history of breast cancer increased the odds of having a mammogram 2.6-fold (P < .05, 95% confidence interval [CI] 1.03 to 6.54). Rates of screening differed sharply by whether a family physician was physically practising in the community or not (P < .05, odds ratio 2.68, 95% CI 1.14 to 6.29). CONCLUSION: Women in FNCs in one health region in New Brunswick have mammography with the same frequency as off-reserve women. A family physician practising part time in the FNCs was instrumental in encouraging women to participate in breast cancer screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".