From “Academic Projectitis” to Partnership: Community Perspectives for Authentic Community Engagement in Health Professional Education
Bibliographic record
Abstract
Health professional education (HPE) has taken a problem-based approach to community service-learning with good intentions to sensitize future health care professionals to community needs and serve the underserved. However, a growing emphasis on social responsibility and accountability has educators rethinking community engagement. Many institutions now seek to improve community participation in educational programs. Likewise, many Canadians are enthusiastic about their health care system and patients, who are “experts by lived experience,” value opportunities to “give back” and improve health care by taking an active role in the education of health professionals. We describe a community-based participatory action research project to develop a mechanism for community engagement in HPE at the University of British Columbia (UBC). In-depth interviews and a community dialogue with leaders from 18 community-based organizations working with vulnerable populations revealed the shared common interest of the community and university in the education of health professionals. Patients and community organizations have a range of expertise that can help to prepare health practitioners to work in partnership with patients, communities, and other professionals. Recommendations are presented to enhance the inclusion of community expertise in HPE by changing the way the community and university engage with each other.
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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 | 0.046 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.057 | 0.095 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.005 | 0.054 |
| Research integrity | 0.009 | 0.022 |
| 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".