Educator perceptions of the relationship between education innovations and improved health
Bibliographic record
Abstract
BACKGROUND: Education innovations by health professions faculty are shaped by faculty conceptualizations of the pathway between their innovations and changes in health of communities. AIMS: We aimed to explore how existing theories about the relationship between education and health are attended to, interpreted, and applied by faculty in different national contexts. METHODS: We compared existing theoretical frameworks to perceptions of "front line" faculty. Fellows in Brazil- and India-based FAIMER faculty development programs were asked via questionnaires about the contribution of their education innovation projects to health improvements. RESULTS: Faculty identified pathways to improved societal health via increased quality, and to a lesser extent relevance, of education. Relationships between increased quantity of education and improved health were focused on faculty development. Faculty from both countries noted the value for health outcomes of innovations that affect networks and partnerships with other institutions. Faculty from India identified pathways to improved societal health via changes to instructional more than institutional processes. CONCLUSIONS: Results indicate where there are gaps in existing theories, a need to raise awareness about potential pathways to improving health via education changes, and opportunities for more detailed understanding of mechanisms of change via in-depth research.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".