Exploring the factors that influence the ratio of generalists to other specialists in Canada.
Bibliographic record
Abstract
OBJECTIVE: To explore perceptions about the factors that influence the ratio of generalists to other specialists. DESIGN: Semistructured interviews. SETTING: Canada. PARTICIPANTS: Thirteen individuals who were closely involved in medical education and health human resource planning or had a role in influencing medical education policy. METHODS: Telephone interviews were conducted with participants until data saturation was reached. Interviews were transcribed and analyzed using constant comparison techniques. For the purpose of simplifying discourse, family medicine and generalism were treated as synonymous throughout the interviews. MAIN FINDINGS: Seven themes emerged from participants' responses: ratio of generalists to specialists, importance of generalism, barriers to generalism, role of the medical education system, role of policy makers, geographic location, and the future of generalism. CONCLUSION: Most respondents perceived the ratio of specialists to generalists as roughly even and believed the reasons for this balance included increased attention from policy makers, a greater presence of family physicians in research and teaching, and a shift toward a more regional and representative distribution of medical education facilities. Respondents also highlighted challenges within family medicine including providers choosing a narrower scope of practice, a shift away from generalism, and ongoing inequities between family physicians and other specialties in terms of remuneration, lifestyle, and prestige.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".