[Geographical distribution of family physicians: which solutions for a complex problem?].
Bibliographic record
Abstract
AIM: This article examines the geographical distribution of family physicians, focusing on attraction issues. METHODS: This analysis is based on a configurational approach. In simple terms, this approach stipulates that the impacts of an intervention are related, on the one hand, to the internal consistency between the characteristics of an intervention and, on the other hand, the consistency between this intervention and its context. A longitudinal case study was performed, corresponding to the Quebec experience over a 35-year period. RESULTS: The measures implemented essentially consisted of training, incentives (positive and negative), support, and, since 2004, a certain degree of coercion. Note that selection of applicants for medicine training programmes according to certain individual variables likely to have an impact on the subsequent site of practice, were only rarely used. An improvement of the efficacy of the combination of measures was observed over time: this improvement can be interpreted in terms of the consistency between the characteristics of the intervention and the consistency between the intervention and its context. CONCLUSION: Interventions designed to promote a more balanced distribution of healthcare professionals cannot be limited to activation of a single lever, but must be considered in the context of complex interventions.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".