Medical students, money, and career selection: students' perception of financial factors and remuneration in family medicine.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medical students have had a declining interest in family medicine as a career. Some studies have shown a small inverse relationship between debt levels and primary care, but it is unclear how students perceive remuneration in different specialties and how these perceptions might influence career choice. METHODS: Medical students at one school were surveyed to understand their perceptions of physician remuneration and to gain insight into how these perceptions might affect career selection. RESULTS: Response rate was 72% (560/781 students). Students' estimates of physician income were accurate throughout training, with the overall estimate for family medicine being lower than the actual income by only $10,656. The vast majority of students agreed with the statement that family physicians get paid too little (85%-89% of each class). The importance of payment as a factor in career decision making increased with higher debt and with advancing training. CONCLUSIONS: Students are able to accurately predict income by specialty from an early stage of training and have a negative perception of income in family medicine. The perception that family physicians make too little money could be an important driver--or at least a modifier--in the lack of interest in family medicine.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".