Facteurs influençant le choix de la spécialité « Médecine Générale » chez les étudiants en deuxième cycle d’études médicales
Bibliographic record
Abstract
Despite a steady increase in the number of doctors in France, the ageing of both the population and the caregivers is increasing the number of areas with low medical coverage. General Practice, a primary care specialty, is attracting fewer and fewer students, particularly in the Ile-de-Franceregion. This lack of interest requires a clear understanding of the motivations behind choosing this specialty. The objective of our study was to explore the various factors determining the choice of future specialty using Beaulieu's Quebecois and French-language questionnaire, which is validated by the literature and allows for surveying a large sample of medical students. The questionnaire, previously adapted to reflect French issues, was sent to three cohorts of medical students from three universities in the Paris region. I received responses from 792 students, 19.82% of whom wished to become general practitioners. A factor analysis and logistic regression were conducted. The factor analysis led to the formation of certain factors that differed from those obtained by the original authors, as well as the isolation of certain items from the questionnaire, which we treated individually. Our logistic regression model explained more than 60% of the variance. The desire to become a general practitioner was significantly associated with having completed an outpatient general medicine internship during the French medical school second cycle, and was motivated by a societal commitment and reduced training constraints. Conversely, factors such as the limited scope of practice, hospital career path orientation or completing an internship in the desired future specialty had a significantly negative influence on the choice of General Practice. These results reflect a lack of representation of General Practice, even though the choice appeared to be mainly driven by desires for flexibility and freedom.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".