Are Family Physicians Educated about Men’s Health?
Bibliographic record
Abstract
Objective The goal of this study was to explore what quantity and quality of training family doctors currently receive in the field of men’s health. Methods: A mixed-methodology was used. A quantitative survey was send to program directors of Canadian family medicine training programs. This was followed by qualitative interviews of selected program directors and two focus groups with practicing family physicians. Program directors from all 17 family medicine training programs in Canada were surveyed. One focus group consisted of family physicians in urban practice and the other group had family physicians in a rural setting. A case study method with a sequential transformative strategy was utilized. Quantitative data were analysed for frequencies and relationships between variables were determined using chi-squares. The qualitative data were thematically analysed through a deductive process. Results: Very few of the 17 training programs had any structured curriculum in men’s health. The focus group participants also reported a lack of any formal men’s health training. Exposure to men’s health topics were sporadic and preceptor dependent. Six different themes were identified: current men’s health teaching in programs, previous men’s health training, need for a curriculum, different mental and physical disease topics, gender differences and procedures related to men’s health. Conclusion: Very little formal training in men’s health takes place in family medicine training programs in Canada.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".