Mobility trends in Psychiatry trainees: an Italian perspective
Bibliographic record
Abstract
BACKGROUND: Psychiatry has been affected by the 'Brain Drain' phenomenon for decades, with professionals usually migrating from lower- to higher-income countries. Whilst Italy faces a decreasing Psychiatric workforce in the near future, little is known about the factors that influence migration of Psychiatry trainees in Italy. AIM: To explore the migration tendencies of Psychiatry trainees training in Italy. METHODS: A cross-sectional survey was disseminated to Psychiatry trainees in Italy. RESULTS: The vast majority (84.2%) of the trainees had 'ever' considered leaving Italy, and more than half (60.4%) considered leaving the country 'now'. Only a quarter (25.3%) had taken 'practical steps' towards migration. Male trainees were more likely to have 'ever' considered leaving Italy. Trainees without children were more likely to have 'ever' considered leaving and more likely to consider leaving 'now'. More southern Italian trainees were considering leaving the country 'now' compared to those from the centre-north. 'Academic' and 'work' reasons were the two most cited factors given both as a reason for wanting to leave Italy and as conditions that should be improved in the country. The main reason cited to remain in the country was personal. CONCLUSIONS: Several Psychiatry trainees in Italy consider migration as a possibility, mainly driven by work and academic reasons. The main factor keeping trainees in Italy was personal reasons. Highlighting the reasons why trainees leave is crucial to facing these issues and either finding ways to encourage trainees to remain or finding other solutions for the medical shortage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".