Migration Of Psychiatric Trainees In Italy
Bibliographic record
Abstract
Background and Aims:In Europe high number of psychiatric trainees have ever considered moving to another country, especially for salary differences. Although in Italy workforce migration is widespread in different fields, little is known about migration of health professionals at an early career stage.This study aims to identify experiences and attitudes towards international migration among Italian psychiatry trainees.Methods:An online survey was conducted among psychiatry trainees from Italy as part of the EFPT Brain Drain Research study.Results:Of 121 psychiatric trainees surveyed across Italy, the majority has u2018everu2019 considered living abroad. Still, just a quarter took u2018practical stepsu2019 towards migration, with male trainees considering migration more than female. Few trainees had ever had a long-term migratory experience or a short-term mobility experience. Academic was an important reason for trainees to leave and personal a key reason for trainees to stay. Across Italy we found wide differences concerning their 5 year plan: the majority of trainees in the south believe they will be working abroad, whereas most of trainees in the north and central part of the country think they will be working in Italy.Conclusions:Many Italian psychiatric trainees have considered moving to another country, especially for academic reasons. These findings call to improve opportunities, particularly in the south part of the country, and help to better understand the social and demographic variations in Italy that may play a role in these migratory flows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".