Characteristic profiles among students and junior doctors with specific career preferences
Bibliographic record
Abstract
BACKGROUND: Factors influencing specialty choice have been studied in an attempt to find incentives to enhance the workforce in certain specialties. The notion of "controllable lifestyle (CL) specialties," defined by work hours and income, is gaining in popularity. As a result, many reports advocate providing a 'lifestyle-friendly' work environment to attract medical graduates. However, little has been documented about the priority in choosing specialties across the diverse career opportunities.This nationwide study was conducted in Japan with the aim of identifying factors that influence specialty choice. It looked for characteristic profiles among senior students and junior doctors who were choosing between different specialties. METHODS: We conducted a survey of 4th and 6th (final)-year medical students and foundation year doctors, using a questionnaire enquiring about their specialty preference and to what extent their decision was influenced by a set of given criteria. The results were subjected to a factor analysis. After identifying factors, we analysed a subset of responses from 6th year students and junior doctors who identified a single specialty as their future career, to calculate a z-score (standard score) of each factor and then we plotted the scores on a cobweb chart to visualise characteristic profiles. RESULTS: Factor analysis yielded 5 factors that influence career preference. Fifteen specialties were sorted into 4 groups based on the factor with the highest z-score: "fulfilling life with job security" (radiology, ophthalmology, anaesthesiology, dermatology and psychiatry), "bioscientific orientation" (internal medicine subspecialties, surgery, obstetrics and gynaecology, emergency medicine, urology, and neurosurgery), and "personal reasons" (paediatrics and orthopaedics). Two other factors were "advice from others" and "educational experience". General medicine / family medicine and otolaryngology were categorized as "intermediate" group because of similar degree of influence from 5 factors. CONCLUSION: What is valued in deciding a career varies between specialties. Emphasis on lifestyle issues, albeit important, might dissuade students and junior doctors who are more interested in bioscientific aspects of the specialty or have strong personal reasons to pursue the career choice. In order to secure balanced workforce across the specialties, enrolling students with varied background and beliefs should be considered in the student selection process.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".