Navigating early career intentions: A qualitative study of influences on specialty choices for medical students
Bibliographic record
Abstract
BACKGROUND: Medical students' career intentions and choices are shaped early in their education, at a time when their interaction with various specialties and professional influences is both formative and essential. Despite this being a pivotal period, the literature offers limited insights into what drives students' specialty choices during these early stages. Our study seeks to address this gap by exploring how medical trainees engage in sensemaking around specialty choice, navigating the interplay between individual aspirations, institutional contexts and perceived professional expectations. METHODS: We conducted an interpretive descriptive study with two consecutive student cohorts at a francophone university in Canada during the implementation of a new medical campus site. Using purposive convenience and snowball sampling, we held 10 focus groups (in-person and virtual): six with first- and second-year medical students and four with clinical teachers. Inductive thematic analysis was employed to interpret the data, enabling us to identify key patterns and relationships between participant perspectives. RESULTS: The participants' perspectives organised around five key themes including (a) navigating career indecision and decision-making processes, (b) role of lifestyle, work-life balance, and career sustainability, (c) role of early educational experiences in career selection, (d) influence of mentorship and role models on career orientation, and (e) hidden curriculum and perceptions of specialty prestige. CONCLUSION: This study offers insights into the factors influencing medical students' specialty choices early in their training. By identifying actionable elements within the undergraduate medical curriculum and the broader learning environment, training programmes can better support students in making well-informed career decisions.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".