Migration of Junior Doctors: The Case of Psychiatric Trainees in Portugal
Bibliographic record
Abstract
INTRODUCTION: In the last few decades, the rates of international medical migration have continuously risen. In Psychiatry, there is great disparity in the workforce between high and low-income countries. Yet, little is known about the 'push' and 'pull' factors and the migratory intentions of trainees. This study aims to assess the factors impacting the decisions of psychiatric trainees in Portugal towards migration. MATERIAL AND METHODS: A questionnaire was developed in the Brain Drain study and was distributed to psychiatric trainees in Portugal. RESULTS: The sample consists of 104 psychiatric trainees (60.6% female). Overall, 40.4% of the trainees had prior experience of living abroad and the majority (96.9%) felt that this experience influenced their attitude towards migration in a positive way. About 75% of trainees had 'ever' considered leaving the country, but the majority (70.0%) had not taken any 'practical steps' towards migration. The main reasons to stay in Portugal were personal, while the main reason to leave was financial. The majority of the trainees (55.7%) were dissatisfied or very dissatisfied with their income, working conditions and academic opportunities. DISCUSSION: Working conditions, salaries and academic opportunities are the main triggers for the migration of psychiatric trainees from Portugal. CONCLUSION: These results may inform the decisions of stakeholders in the health and education sectors and point out the necessary investments required and the impact it may have on the workforce.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".