Educational exposures associated with preclinical medical student interest in pursuing surgical residency: Longitudinal mixed-methods study with narrative evaluation
Bibliographic record
Abstract
Introduction: Pre-clerkship medical students rely on various educational experiences to decide on the residency they would like to pursue. We conducted a longitudinal mixed-methods study to identify educational experiences in pre-clerkship that are associated with an interest in pursuing surgery. Methods: Pre-clerkship medical students were invited to complete an initial survey regarding their interest in surgery and educational exposures. After 10 months, a follow-up survey was sent to identify changes in their interest and the role of educational experiences they may have had in the interim. Univariate regression was used to determine associations, and thematic analysis was done. Results: Data from 218 pre-clerkship students showed that shadowing (OR = 2.7), participation in technical workshops (OR = 5.1), having a mentor (OR = 4.6) and conducting surgical research (OR = 4.6) were associated with an interest in pursuing surgery. From the students with follow-up data, thematic analysis showed that 94 %, 89 %, and 81 % of students found shadowing, research, and mentorship, respectively, as influential in the decision of pursuing a surgical specialty, respectively. Conclusions: Shadowing and mentorship were important factors for students in the decision-making process in pursuing surgery. Identifying high-yield educational experiences-for students to determine if one wants to pursue a surgical specialty is important for educators in curriculum design for resource allocation. Key message: We describe a longitudinal mixed-methods study to determine the role of early educational exposures which influence a medical student's decision to pursue a surgical specialty. Shadowing, technical skills workshops, surgical mentorship, involvement in surgical research, play an important role for student 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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".