Evaluation of a pilot rural mentorship programme for and by pre-clerkship medical students
Bibliographic record
Abstract
INTRODUCTION: While medical school interventions can help address rural physician shortages, many urban Canadian medical students lack exposure to rural medicine. The Rural Mentorship Programme (RMP) is a 4-month pilot initiative designed by medical students to bridge this gap by pairing preclerkship medical students at an urban medical school with rural physician mentors to provide exposure to rural careers. METHODS: A realist-influenced methodology evaluated perceived benefits and challenges of RMP, assessed how RMP influenced mentee perceptions and intentions towards rural careers, and investigated factors leading to success. Quantitative and qualitative data were collected through evaluative pre-, post-, and 4-month post intervention surveys, mentor interviews and a mentee focus group. Likert scales assessed satisfaction, attainment of objectives and mentee changes in perceptions and intentions. RESULTS: 18/23 mentees and 11/15 mentors completed at least 1 survey; 5 mentees joined the focus group and 3 mentors were interviewed. Most mentees were of non-rural backgrounds and initially neutral about pursuing rural practice. RMP helped mentees better understand rural careers. They especially valued the mandatory community clinical visit and forming relationships with mentors. Mentors enjoyed teaching, reflecting on their careers and demonstrating the merits of rural practice. Transportation and scheduling were major programme challenges. CONCLUSIONS: This pilot suggests that structured mentorship programmes can improve understanding of, and provide exposure to, careers in rural medicine for urban medical students. Results will inform future programme development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".