Where have all the residents gone? Part 2: Renewing interest in family medicine (continued from June 2006)
Bibliographic record
Abstract
Various factors have contributed to the recent decline in applications to family medicine residency pro-grams in Canada. The first article in this series1 described some of the reasons medical students are not choosing family medicine, such as perceived low prestige, heavy workloads, and breadth of knowledge required. It also explored this trend in the context of increasing finan-cial pressures involved in studying medicine. Perhaps 2006 will be a banner year for family medicine. Newly implemented strategies could well revive enthusiasm for family medicine despite deteriorating student interest in the discipline. The College of Family Physicians of Canada has already taken the initiative by creating scholarships and prizes for medical students showing strong interest in family medicine. The criteria for these awards must be augmented to include Canadians studying medicine abroad. These stu-dents number in the hundreds at medical schools in the United States, Europe, and Australia2,3; their repatriation should be encouraged. Furthermore, the awards should be partly contingent on beginning a residency in family medi-cine; anecdotal evidence suggests that a few award recipi-ents do not actually pursue careers in family medicine. The awareness campaign surrounding Family Doctor Week is another innovative idea that builds on Canadians ’ consider-able trust in their GPs. The Canadian Federation of Medical
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".