Effect of a surgical observership on the perceptions and career choices of preclinical medical students: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Medical students are increasingly choosing nonsurgical specialties; observership programs can address factors influencing them toward surgical careers by allowing preclerkship exposure and mentorship, and correcting misconceptions. The aims of this study were to assess the influence of a peer-led observership program at the Université de Montréal on the career choices of preclinical medical students and to determine the factors associated with a positive observership experience. METHODS: We used a quasi-experimental convergent mixed-methods questionnaire design. From Nov. 19 to Dec. 31, 2018, and Mar. 1 to Apr. 4, 2019, all medical students participating in the observership program were eligible for the study; there were no ineligibility criteria. Using a prospective purposive sampling method, we recruited students via the email sent to confirm their observership. In the week after their observership, we invited the students by email to complete a postintervention survey. We used nonparametric tests to evaluate the impact of the observership on participants' career choices and an inductive data-driven thematic analysis to analyze their responses. RESULTS: Of the 204 students who participated, 157 provided consent, of whom 85 (54.1%) completed questionnaires both before and after the observership. The majority of participants were interested in a surgical specialty before (72 [85%]) and after (68 [84%]) the observership. There was no significant change in the students' choices of surgical specialties after the observership. However, most (68 [81%]) reported being more interested in a surgical career as a result of the observership, which allowed them to see that the type of practice they considered was congruent with a surgical career. Their perceptions of the field of surgery became positive, particularly regarding its pace and atmosphere and the humanistic patient-doctor relationship it required. The experience was influenced by surgeons' and teams' attitudes toward students, knowledge-sharing and quality of exposure. Participants mentioned that their willingness to participate was in part responsible for the success of their experience. CONCLUSION: This observership program allowed an early, positive introduction of students to surgery while challenging stereotypes. It provided a better understanding of surgery, enabling participants to consider this field and potentially influencing their residency application.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".