The community comes to campus: the Patient and Community Fair
Bibliographic record
Abstract
BACKGROUND: Community-based learning connects students with local communities so that they learn about the broad context in which health and social care is provided; however, students usually interact with only one or a few organisations that serve a particular population. One example of a community-based learning activity is the health fair in which students provide health promotion and screening for local communities. CONTEXT: We adapted the health fair concept to develop a multi-professional educational event at which, instead of providing service, students learn from and about the expertise and resources of not-for-profit organisations. INNOVATION: The fair is an annual 1-day event that students can attend between, or in place of, classes. Each community organisation has a booth to display information. One-hour 'patient panels' are held on a variety of topics throughout the day. Evaluation methods include questionnaires, exit interviews and visitor tracking sheets. Over 5 years (2009-2013), the fair increased in size with respect to estimated attendance, number of participating organisations, number of patient panels and number of students for whom the fair is a required curriculum component. Students learn about a range of patient experiences and community resources, and information about specific diseases or conditions. IMPLICATIONS: The fair is an efficient way for students to learn about a range of community organisations. It fosters university-community engagement through continuing connections between students, faculty members and community organisations. Lessons learned include the need for community organisations to have techniques to engage students, and ways to overcome challenges of evaluating an informal 'drop-in' event. The fair is an efficient way for students to learn about a range of community organisations.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".