Profiling recent medical graduates planning to pursue surgery, anesthesia and obstetrics in Brazil
Bibliographic record
Abstract
BACKGROUND: Lack of providers in surgery, anesthesia, and obstetrics (SAO) is a primary driver of limited surgical capacity worldwide. We aimed to identify predictors of entry into Surgery, Anesthesia, and Obstetrics and Gynecology (SAO) fields and preference of working in the public sector in Brazil which may help in profiling medical students for recruitment into these needed areas. METHODS: A questionnaire was applied to all Brazilian medical graduates registered with a Board of Medicine from 2014 to 2015. Twenty-three characteristics were analyzed. Logistic regression was used to determine predictors' influence on outcome. RESULTS: There were 4601 (28.2%) responders to the survey, of which 40.5% (CI 34.7-46.5%) plan to enter SAO careers. Of the 23 characteristics analyzed, eight differed significantly between those who planned to work in SAO and those who did not. Of those eight characteristics, just three were significant predictors in the regression model: preference for working in the hospital setting, having spent more than 70% of their clinical years in practical activities, and valuing the substantial earning potential. These three factors explained only 6.3% of the variance in SAO preference. Within the graduates who preferred SAO careers, there were only two predictors for working in the public sector ("preparatory time before medical school" and valuing "prestige/status"). CONCLUSIONS: Factors affecting specialty and sector choice are multifaceted and difficult to predict. Future programs to fill provider gaps should identify methods other than medical student profiling to assure specialty and sector needs are met.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".