Prepared to practice? Perception of career preparation and guidance of recent medical graduates at two campuses of a transnational medical school: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Graduating medical students enter the workforce with substantial medical knowledge and experience, yet little is known about how well they are prepared for the transition to medical practice in diverse settings. We set out to compare perceptions of medical school graduates' career guidance with their perceptions of preparedness to practice as interns. We also set out to compare perceptions of preparedness for hospital practice between graduates from two transnational medical schools. METHODS: This was a cross-sectional study. A Preparedness for Hospital Practice (PHPQ) survey and career guidance questionnaire was sent to recent medical graduates, incorporating additional free text responses on career preparation. Data was analyzed using descriptive statistics and tests of association including Chi-square, Mann-Whitney U and Kruskal-Wallis H tests. RESULTS: Forty three percent (240/555) of graduates responded to the survey: 39 % of respondents were domestic (Dublin, Ireland or Manama, Kingdom of Bahrain) and interning locally; 15 % were overseas students interning locally; 42 % were overseas students interning internationally and 4 % had not started internship. Two variables explained 13 % of the variation in preparedness for hospital practice score: having planned postgraduate education prior to entering medical school and having helpful career guidance in medical school. Overseas graduates interning internationally were more likely to have planned their postgraduate career path prior to entering medical school. Dublin graduates found their career guidance more helpful than Bahrain counterparts. The most cited shortcomings were lack of structured career advice and lack of advice on the Irish and Bahraini postgraduate systems. CONCLUSIONS: This study has demonstrated that early consideration of postgraduate career preparation and helpful medical school career guidance has a strong association with perceptions of preparedness of medical graduates for hospital practice. In an era of increasing globalization of medical education, these findings can direct ongoing efforts to ensure all medical students receive career guidance and preparation for internship appropriate to their destination.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".