Deeper Learning through Service: Evaluation of an Interprofessional Community Service-Learning Program for Pharmacy and Medicine Students
Bibliographic record
Abstract
Abstract Background This Community Service-Learning Project (CSLP) at the University of Saskatchewan is designed to help students develop patient-centred care practices in urban underserved settings. First-year medical and pharmacy students partner interprofessionally to both learn and serve, working with community-based organizations (CBOs) that primarily serve either low-income or newcomer residents of Saskatoon. Since the CSLP’s pilot year in 2005-2006, 98 first-year medical and pharmacy students have participated in the CSLP. Methods and Findings We evaluated the outcomes and processes of the CSLP since the 2006-2007 year, using mixed methods: end-of-project questionnaires; document analysis looking for key and recurrent themes; end-of-project semi-structured interviews with CBO coordinators and clients. We examined students’ experiences, including satisfaction, achievement of learning objectives, learning processes, and perceived outcomes. Students’ main learning outcomes related to client-centered approach, interprofessional attitudes and skills, and personal development. Various learnings related to program processes are reported. Conclusions Our evaluation reinforced findings from the literature on both interprofessional education and community service-learning, as well uncovering some new findings. Students described a transformative learning experience that helped them begin to develop understanding and skills to work more effectively with clients in urban underserved settings.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".