Why you should Mini-Med School: Mini-Med School as an intervention to increase health literacy
Bibliographic record
Abstract
BACKGROUND: Health literacy is an increasingly important topic in healthcare given that low health literacy is widely prevalent and linked to poorer health outcomes and higher healthcare costs. We sought to determine if a Mini-Med School delivered by medical students could prove to be an effective intervention to improve health literacy in the elderly. METHODS: This study took place in the context of the University of British Columbia Medical Doctorate Undergraduate Program's Flexible and Enhanced Learning course. It aimed to evaluate the effectiveness of a Mini-Med School lecture series as an intervention to increase health literacy in 24 volunteer participants from the University of Victoria Retirees Association. This was a cross sectional study comparing health literacy pre- and post-intervention using the validated Health Literacy Questionnaire. RESULTS: There was a statistically significant improvement in seven of nine scales of health literacy when participants repeated the Health Literacy Questionnaire six weeks post-intervention as well as positive outcomes from both a student learning and community outreach perspective. DISCUSSION: This study demonstrates that a Mini-Med School program is an effective way to increase health literacy; adds to the minimal research surrounding Mini-Med Schools; and should further encourage Canadian medical schools to use Mini-Medical Schools as a method of engagement and advocacy with their communities.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".