Introducing global health into the undergraduate medical school curriculum using an e-learning program: a mixed method pilot study
Bibliographic record
Abstract
BACKGROUND: Physicians need global health competencies to provide effective care to culturally and linguistically diverse patients. Medical schools are seeking innovative approaches to support global health learning. This pilot study evaluated e-learning versus peer-reviewed articles to improve conceptual knowledge of global health. METHODS: A mixed methods study using a randomized-controlled trial (RCT) and qualitative inquiry consisting of four post-intervention focus groups. Outcomes included pre/post knowledge quiz and self-assessment measures based on validated tools from a Global Health CanMEDS Competency Model. RCT results were analyzed using SPSS-21 and focus group transcripts coded using NVivo-9 and recoded using thematic analysis. RESULTS: One hundred and sixty-one pre-clerkship medical students from three Canadian medical schools participated in 2012-2013: 59 completed all elements of the RCT, 24 participated in the focus groups. Overall, comparing pre to post results, both groups showed a significant increase in the mean knowledge (quiz) scores and for 5/7 self-assessed competencies (p < 0.05). These quantitative data were triangulated with the focus groups findings that revealed knowledge acquisition with both approaches. There was no statistically significant difference between the two approaches. Participants highlighted their preference for e-learning to introduce new global health knowledge and as a repository of resources. They also mentioned personal interest in global health, online convenience and integration into the curriculum as incentives to complete the e-learning. Beta version e-learning barriers included content overload and technical difficulties. CONCLUSIONS: Both the e-learning and the peer reviewed PDF articles improved global health conceptual knowledge. Many students however, preferred e-learning given its interactive, multi-media approach, access to links and reference materials and its capacity to engage and re-engage over long periods of time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".