Influencing Student Beliefs about Poverty and Health through Interprofessional Community-Based Educational Experiences
Bibliographic record
Abstract
Background: Pre-licensure students from medicine, physical therapy, kinesiology, nursing, and social work participated in a population health project at the University of Saskatchewan. We assessed the effect of this interactive, interprofessional, community-based educational experience on students’ attitudes and beliefs about poverty and health.Methods and Findings: Participants (N = 119) completed two measures at the beginning and end of the five-week project: the 37-item Attitudes toward Poverty Scale (APS) and the 8-item Beliefs about the Relationship between Poverty and Health (BRPH). APS scores showed a modest significant increase toward more positive attitudes over time (F(1, 110) = 7.97, p < .01). On the BRPH, participants agreed significantly less at Week 5 with two behavioral explanations (F(1, 114) = 5.07, p < .05; F(1, 114) = 11.00, p < .01) and one structural explanation (F(1, 112) = 11.09, p < .01) about relationships between poverty and health. There was some evidence that face-to-face interactions with community members had more impact than a simulation exercise. Students gave positive evaluations of the interprofessional format of the project. Attrition effects may limit the interpretation of these results.Conclusions: Results demonstrate that brief interprofessional community-based learning experiences can positively influence students’ attitudes and beliefs about the relationship between poverty and health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".