Working women identify influences and obstacles to breast health practices.
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify factors contributing to participation in breast screening in working women to drive health education planning and implementation. DESIGN: Survey. SETTING: Automotive plants in southern Canada. SAMPLE: Union and nonunion women working in the plants. METHODS: Survey using "Health Care Practices: A Worksite Survey," modified for Canadian population. MAIN RESEARCH VARIABLES: Age, education, breast health practices, influences on decision to participate in breast screening, and physician gender. FINDINGS: Differences were noted among three age groups (under 30 years, 30-49 years, 50 years or older) in terms of influences and perceived barriers to the different modalities of breast screening. For clinical breast exams, women preferred an expert in breast health, regardless of whether the professional was a physician or a nurse. In all groups, the physician was noted as being very influential; however, perceptions of encouragement from the physician varied across the age groups. Perceptions of barriers to breast screening differed among the age groups and between women with male physicians and those with female physicians. Coworkers were identified as being a strong influence in the older group, whereas friends and family were identified as being more influential in the younger groups. CONCLUSIONS: Health promotion and education strategies may need to be stratified for different age groups. IMPLICATIONS FOR NURSING PRACTICE: Breast health education may need to be seen as an ongoing educational process, with the target groups being both the women and the primary healthcare professionals. The worksite has strong potential as a setting for health promotion activities.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".