Peer-led learning: a novel approach to promote rural healthcare interest among medical students
Bibliographic record
Abstract
Introduction: A persistent maldistribution of medical workforce exists across Canada, with rural areas facing a greater physician shortage. Medical education can be instrumental to increase physicians in rural communities, and medical schools have adapted strategies to generate interest in rural careers among medical students. Many of these efforts occur within formal structured curriculum. This study appraises the effectiveness of peer-led learning (PLL) as a novel approach in rural medical education to provide students with a better understanding of rural life and rural medical practice. Methods: This is mixed methods study using a survey and follow-up focus group discussion to evaluate a day-long educational experience organized and led by a medical student to their rural community. Quantitative data were summarized with descriptive statistics. Reflexive thematic analysis was conducted on qualitative insights to describe the students' experiences and perceptions about the educational rural day. Results: Of 54 participants, 50 completed the survey and 13 consented for the follow-up focus group. Most (78%) were female, have non-rural origins (78%), with only 2 having Indigenous status. Majority (61%) have low familiarity with rural medicine. Trustworthiness scores for information about rural life and medical practice were higher for rural-origin peers and rural-origin faculty compared to other sources of information such as government websites, social media, and traditional media. Thematic analysis yielded three main themes: (i) informal teaching facilitated learning, (ii) trust in their peer enabled students to receive information more favorably, and (iii) students gained a better understanding of rural life and medical practice. Conclusion: This study demonstrated that medical students engage differently with peer-led learning activities about rural medical curriculum versus a formal teaching environment. Medical students are cautious about promotional information regarding rural medical education from formal sources but are less skeptical when learning from peers. Information about the way of life and healthcare needs in rural communities may be perceived as more credible and valid if coming from a peer, and hence, is more likely to be received favorably. Thus, when promoting rural education and careers, medical schools should work with rural-origin students, whose messaging may be considered more trustworthy than traditional sources.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".