Life in unexpected places: Employing visual thinking strategies in global health training
Bibliographic record
Abstract
BACKGROUND: The desire to make meaning out of images, metaphor, and other representations indicates higher-order cognitive skills that can be difficult to teach, especially in the complex and unfamiliar environments like those encountered in many global health experiences. Because reflecting on art can help develop medical students' imaginative and interpretive skills, we used visual thinking strategies (VTS) during an immersive 4-week global health elective for medical students to help them construct new understanding of the social determinants of health in a low-resource setting. We were aware of no previous formal efforts to use art in global health training. METHODS: We assembled a group of eight medical students in front of a street mural in Kathmandu and used VTS methods to interpret the scene with respect to the social determinants of health. We recorded and transcribed the conversation and conducted a thematic analysis of student responses. RESULTS: Students shared observations about the mural in a supportive, nonjudgmental fashion. Two main themes emerged from their observations: those of human-environment interactions (specifically community dynamics, subsistence land use, resources, and health) and entrapment/control, particularly relating to expectations of, and demands on, women in traditional farming communities. They used the images as well as their experience in Nepali communities to consolidate complex community health concepts. DISCUSSION: VTS helped students articulate their deepening understanding of the social determinants of health in Nepal, suggesting that reflection on visual art can help learners apply, analyze, and evaluate complex concepts in global health. We demonstrate the relevance of drawing upon many aspects of cultural learning, regarding art as a kind of text that holds valuable information. These findings may help provide innovative opportunities for teaching and evaluating global health training in the future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".