Factors Affecting Specialty Training Preference Among UK Medical Students (FAST): Protocol for a National Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: The UK medical education system faces a complex landscape of specialty training choices and heightened competition. The Factors Affecting Specialty Training Preference Among UK Medical Students (FAST) study addresses the need to understand the factors influencing UK medical students' specialty choices, against a backdrop of increasing challenges in health care workforce planning. OBJECTIVE: The primary objectives of the FAST study are to explore UK medical students' preferred specialties and the factors that influence these choices. Secondary objectives are to evaluate students' confidence in securing their chosen specialty, to understand how demographic and academic backgrounds affect their decisions, and to examine how specialty preferences and confidence levels vary across different UK medical schools. METHODS: A cross-sectional survey design will be used to collect data from UK medical students. The survey, comprising 17 questions, uses Likert scales, multiple-choice formats, and free-text entry to capture nuanced insights into specialty choice factors. The methodology, adapted from the Ascertaining the Career Intentions of UK Medical Students (AIMS) study, incorporates adjustments based on literature review, clinical staff feedback, and pilot group insights. This approach ensures comprehensive and nondirective questioning. Data analysis will include descriptive statistics to establish basic patterns, ANOVA for group comparisons, logistic regression for outcome modeling, and discrete choice models for specialty preference analysis. RESULTS: The study was launched nationally on December 4, 2023. Data collection is anticipated to end on March 1, 2024, with data analysis beginning thereafter. The results are expected to be available later in 2024. CONCLUSIONS: The FAST study represents an important step in understanding the factors influencing UK medical students' career pathways. By integrating diverse student perspectives across year groups and medical schools, this study seeks to provide critical insights into the dynamics of specialty, or residency, selection. The findings are anticipated to inform both policy and educational strategies, aiming to align training opportunities with the evolving needs and aspirations of the future medical workforce. Ultimately, the insights gained may guide initiatives to balance specialty distribution, improve career guidance, and improve overall student satisfaction within the National Health Service, contributing to a more stable and effective health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55155.
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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 | 0.048 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".