Nurturing social responsibility through community service-learning: Lessons learned from a pilot project
Bibliographic record
Abstract
BACKGROUND: Community service-learning (CSL) has been proposed as one way to enrich medical and dental students' sense of social responsibility toward people who are marginalized in society. AIM: We developed and implemented a new CSL option in the integrated medical/dental curriculum and assessed its educational impact. METHODS: Focus groups, individual open-ended interviews, and a survey were used to assess dental students', faculty tutors' and community partners' experiences with CSL. RESULTS: CSL enabled a deeper appreciation for the vulnerabilities that people who are marginalized experience; students gained a greater insight into the social determinants of health and the related importance of community engagement; and they developed useful skills in health promotion project planning, implementation and evaluation. Community partners and faculty tutors indicated that equal partnership, greater collaboration, and a participatory approach to course development are essential to sustainability in CSL. CONCLUSIONS: CSL can play an important role in nurturing a purposeful sense of social responsibility among future practitioners. Our study enabled the implementation of an innovative longitudinal course (professionalism and community service) in all 4 years of the dental curriculum.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".