A Brief Discussion of the Impact of Service- Learning Experience on Physicians´ Manner of Practice
Bibliographic record
Abstract
Background: Service-learning combines student education with volunteer activities which benefit the community. The authors examined how service-learning through a Canadian medical student-run clinic for refugee newcomers shapes the subsequent practice style of physicians. The clinic (known as “The Gateway Program”) aims to improve access to medical care for refugees in the urban center in which the medical schoolis located, while teaching students about cross-cultural medicine. The connection between current practice styles and prior service-learning has not been well documented in the literature, despite value existing in examining the impact this educational tool has on physicians. The research question was: How does participation in service-learning shape the manner in which physicians subsequently deliver healthcare? Methods: The study qualitatively examines the experiences of former student coordinators of The Gateway Program who are now practicing Canadian physicians. Eight participants were interviewed. Data was subjected to thematic analysis. Results: Three key themes were identified: Advocacy, Social Determinants of Health, and Transferable Skills for Underserved Populations. Conclusion: Time served in the clinic is reported to have had a profound impact on participants’ current style of practice. The clinic exposed students early in their medical education to a functional model of care for the underserved which valued physician advocacy. As practicing professionals, they were able to draw upon this learning to inform their professional morals and style of practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".