Malaysian Medical Students’ Career Intention (MMSCI): a cross-sectional study
Bibliographic record
Abstract
Abstract Background In recent years, there have been many instances of negative sentiments expressed by and resignations observed from doctors working in the Ministry of Health (MOH), Malaysia. However, little is known about the perspectives of medical students and their career intentions. This study aims to determine the current Malaysian medical students’ career intentions immediately after graduation and upon completing the 2 years of housemanship and to establish the factors influencing these intentions. Methods This was a cross-sectional study of 859 Malaysian medical students from 21 medical schools who voluntarily completed a self-administered online questionnaire that was disseminated by representatives from medical schools nationwide and social media platforms of a national medical student society. Results 37.8% of the respondents were optimistic about a career with the Ministry of Health (MOH), Malaysia in the future. Most of the respondents (91.2%) plan to join and complete the MOH Housemanship programme as soon as possible after graduation, with the majority of them (66.2%) planning to complete it in their state of origin. After 2 years of Housemanship programme, only more than half of the respondents (63.1%) plan to continue their careers in MOH. Slightly more than a quarter (27.1%) of the total respondents plan to emigrate to practise medicine, with 80.7% of them planning to return to Malaysia to practise medicine after some years or after completing specialisation training. Combining the career intentions of Malaysian medical students immediately after graduation and upon completion of the 2 years housemanship programme, only a slight majority (57.5%) of the respondents plan to continue their career in MOH eventually. Most of the respondents (85.0%) intend to specialise. Conclusion A concerning number of Malaysian medical students plan to leave the Ministry of Health workforce, the main healthcare provider in Malaysia, in the future. Urgent government interventions are needed to address the underlying factors contributing to the potential exodus of future doctors to prevent further straining of the already overburdened healthcare system, posing a significant threat to public well-being. An annual national study to track medical students’ career intentions is recommended to gather crucial data for the human resources for health planning in Malaysia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".