Career Choices Among Medical Students and Factors Influencing Their Choices
Bibliographic record
Abstract
BACKGROUND: Physicians’ specialty choices have a direct impact on medical workforce. As medical students progress through medical school, it is observed that their interests in specialties change due to one reason or another. The aim of this study is to firstly identify factors that influence medical students’ career choices using a cross sectional study. Secondly, to analyze which factors are the most influential with the aim of informing the work force and curriculum developers and thus enhance the employability of graduates. METHODS: A computer generated random sample of 131 medical students was taken from the preclinical second-year medical students at the Royal College of Surgeons in Ireland-Medical University of Bahrain. Questionnaires were distributed face to face and later retrieved for data collection and analysis. RESULTS: Eighty-four (70%) students responded to the survey. Thirty-two (38.1%) of the respondents were male and fifty-two (61.9%) were female. The top three preferred specialty choices were Surgery 22 (26.5%) followed by Internal medicine 12 (14.5%) and Paediatrics 11 (13.3%). The most popular factor in specialty choice was interesting field and the least popular factor was geographical location of the hospital or health instituation. CONCLUSION: The top three career choices were selected because those students felt they were interesting fields. The least important factors were geographical location of the hospitals or health instituations, media influence and financial reasons respectively.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".