Identifying important breast cancer control strategies in Asia, Latin America and the Middle East/North Africa
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most frequent cause of cancer death in women worldwide, but global disparities in breast cancer control persist, due to a lack of a comprehensive breast cancer control strategy in many countries. OBJECTIVES: To identify and compare the need for breast cancer control strategies in Asia, Latin America and the Middle East/North Africa and to develop a common framework to guide the development of national breast cancer control strategies. METHODS: Data were derived from open-ended, semi-structured interviews conducted in 2007 with 221 clinicians, policy makers, and patient advocates; stratified across Asia (n = 97), Latin America (n = 46), the Middle East/North Africa (ME/NA) (n = 39) and Australia and Canada (n = 39). Respondents were identified using purposive and snowballing sampling. Interpretation of the data utilized interpretive phenomenological analysis where transcripts and field notes were coded and analyzed and common themes were identified. Analysis of regional variation was conducted based on the frequency of discussion and the writing of the manuscript followed the RATS guidelines. RESULTS: Analysis revealed four major themes that form the foundation for developing national breast cancer control strategies: 1) building capacity; 2) developing evidence; 3) removing barriers; and 4) promoting advocacy - each specified across five sub-ordinate dimensions. The propensity to discuss most dimensions was similar across regions, but managing advocacy was discussed more frequently (p = 0.004) and organized advocacy was discussed less frequently (p < 0.001) in Australia and Canada. CONCLUSIONS: This unique research identified common themes for the development of breast cancer control strategies, grounded in the experience of local practitioners, policy makers and advocacy leaders across diverse regions. Future research should be aimed at gathering a wider array of experiences, including those of patients.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".