Qualitative study on the factors leading to variation in experience of the Foundation Psychiatry Fellowship of the Royal College of Psychiatrists
Bibliographic record
Abstract
INTRODUCTION: The Psychiatry Foundation Fellowships were created by the Royal College of Psychiatrists (RCPsych) as a route to encourage foundation doctors to consider psychiatry as an exciting medical discipline. OBJECTIVES: This study aimed to explore the Psychiatry Foundation Fellows’ experience of applying to the Fellowship, their expectations prior to being appointed, the benefits of the fellowship, the barriers to gaining those benefits, any common factors raised, and any suggestions about how to improve the fellowship. METHODS: The researcher was a leadership fellow in medical education and simulation in the Foundation school of East of England. Ethical approval was obtained through Higher Education England as this was a service evaluation. Recruitment was purposive and participants were contacted by a gatekeeper. Four 1:1 interviews took place, the interviews were audio recorded, transcribed and the transcripts were analysed with thematic analysis. RESULTS: Preliminary Themes [Table: see text] CONCLUSIONS: The Psychiatry Foundation Fellowship was generally a positive experience in terms of fostering enthusiasm for psychiatry. A sense of community among fellows and recognition among clinical supervisors in acute trusts were felt to be lacking. The themes were used to shape RCPsych’s future plans for the Psychiatry Foundation Fellowship. DISCLOSURE OF INTEREST: None Declared
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".