Ascertaining the Career Intentions of Medical Students (AIMS) in the United Kingdom Post Graduation: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Among doctors in the United Kingdom, there is growing sentiment regarding delaying specialist training, emigrating to practice medicine abroad, or leaving the profession altogether. This trend may have substantial implications for the future of the profession in the United Kingdom. The extent to which this sentiment is also present in the medical student population is not well understood. OBJECTIVE: Our primary outcome is to determine current medical students' career intentions after graduation and upon completing the foundation program and to establish the motivations behind these intentions. Secondary outcomes include determining which, if any, demographic factors alter the propensity to pursue different career paths available to a medical graduate, determining which specialties medical students plan on pursuing, and understanding current views on the prospect of working in the National Health Service (NHS). METHODS: The Ascertaining the Career Intentions of Medical Students (AIMS) study is a national, multi-institution, and cross-sectional study in which all medical students at all medical schools in the United Kingdom are eligible to participate. It was administered via a novel, mixed methods, and web-based questionnaire and disseminated through a collaborative network of approximately 200 students recruited for this purpose. Both quantitative and thematic analyses will be performed. RESULTS: The study was launched nationally on January 16, 2023. Data collection was closed on March 27, 2023, and data analysis has commenced. The results are expected to be available later in the year. CONCLUSIONS: Doctors' career satisfaction within the NHS is a well-researched topic; however, there is a shortage of high-powered studies that are able to offer insight into medical students' outlook on their future careers. It is anticipated that the results of this study will bring clarity to this issue. Identified areas of improvement in medical training or within the NHS could be targeted to improve doctors' working conditions and help retain medical graduates. Results may also aid future workforce-planning efforts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45992.
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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 | 0.084 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".