Medical Students’ Insight into Foundation Training (MEDSIFT): a National Cross-Sectional Online Survey reveals close to 50% are considering a career outside the NHS
Bibliographic record
Abstract
INTRODUCTION: The new preference-informed allocation (PIA) system introduced for the 2024 UK cohort Foundation Programme (UKFPO) marks a shift away from the traditional meritocratic 'ranking' used in previous years. Instead of appointment to Foundation Programme places, PIA is a computer-generated allocation and deanery preferencing system. This change has raised numerous concerns among both students and clinicians. AIMS: To investigate the opinions of medical students on the new UKFPO PIA system. METHODOLOGY: An online questionnaire was distributed to medical students graduating in 2024, 2025, or 2026 across the UK. RESULTS: In total, 2297 responses were collected and 2288 were included in the study. Overall, 51.6% (n = 1183) of respondents felt the PIA system was unfair, 76.3% (n = 1746) felt they had lost control of their application, and 46.3% (n = 1049) had noticed a negative effect on their physical or mental health. Notably, 48.2% (n = 1094) of students who responded are now considering a career outside the National Health Service (NHS). CONCLUSIONS: Overall, the PIA system falls short of students' expectations and has led to record numbers of students considering careers outside the NHS. Further changes to this system are needed and should aim to address fairness and equity while rewarding students for their hard work. According to these data, the PIA system risks further deteriorating workforce morale and attrition rates.
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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 | 0.003 | 0.011 |
| 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) | 0.007 | 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".