From Uncertainty to Confidence: Peer-Led Research and the Formation of Medical Academic Identity
Bibliographic record
Abstract
Background: Undergraduate research is vital for developing critical thinking and academic identity in medical students, yet traditional models often fail to overcome institutional and personal barriers. Peer-led approaches may offer more accessible, supportive environments that promote deeper engagement and leadership in research. Methods: This study evaluated medical students’ experiences in a peer-led research initiative from 2022 to 2024. Students were then invited to complete a qualitative questionnaire reflecting on their perceptions towards research, development in research skills, confidence, and academic identity. Results: Code saturation was achieved after 9 responses (N = 15). Participants reported intrinsic interest, peer encouragement, and opportunities to publish as motivating factors. The peer-led model made research feel more approachable, fostering technical growth and academic confidence. Peer mentorship and a gradual learning structure were especially valued. While challenges such as workload and team dynamics emerged, students reported growth in resilience and self-reflection. Conclusions: Peer-led research initiatives can effectively support academic identity formation by integrating motivation, support, and skill development. Despite obstacles, students gained competence and confidence.
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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 | 0.067 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.006 |
| 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".