IMAGINE-ing interprofessional education: program evaluation of a novel inner city health educational experience
Bibliographic record
Abstract
BACKGROUND: Poverty is a key determinant of health that leads to poor health outcomes. Although most healthcare providers will work with patients experiencing poverty, surveys among healthcare students have reported a curriculum gap in this area. This study aims to introduce and evaluate a novel, student-run interprofessional inner city health educational program that combines both practical and didactic educational components. METHODS: Students participating in the program answered pre- and post-program surveys. Wilcoxon signed-rank tests and descriptive thematic analysis were used for quantitative and qualitative data, respectively. RESULTS: A total of 28 out of 35 participants responded (response rate: 80%). Student knowledge about issues facing underserved populations and resources for underserved populations significantly increased after program participation. Student comfort working with underserved populations also significantly increased after program participation. Valued program elements included workshops, shadowing, and a focus on marginalized populations. CONCLUSION: Interprofessional inner city health educational programs are beneficial for students to learn about poverty intervention and resources, and may represent a strategy to address a gap in the healthcare professional 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".