Using a Health Equity Lens to Evaluate Short-Term Experiences in Global Health (STEGH)
Bibliographic record
Abstract
Background: The growing popularity of short-term experiences in global health (STEGH) has given rise to increasing criticism around their purported benefits and outcomes. With the global health and development community's growing focus on improving population health and equity worldwide as outlined in the United Nation's Sustainable Development Goals, there is a growing opportunity to examine and optimize the conduct of STEGH using an outcomes and equity focused lens. Objectives: This viewpoint aims to develop a framework that can be used to plan and evaluate STEGH on outcomes underpinned by a health equity focus. Methods: Drawing on logic model theory, the analysis first identifies extant issues and their drivers around the planning, implementation, and evaluation of tradition STEGH (focused on clinical service provision.) The analysis then explores various definitions of health equity, settling on a broad definition around context that promotes health for all as opposed to equity of access to healthcare services. With that definition as the ultimate benchmark of success, the analysis then proposes questions that can be used to determine how and when a STEGH might best be deployed to meet that goal. Findings: Traditional reliance on process outputs from service-based approaches have historically limited an understanding of if and how STEGH might advance health equity. Using an outcomes-focused approach identifies critical questions around the value of such experiences, when weighed against a broad definition of equity and other key global health themes such as sustainability, cultural humility, and impact. Measuring STEGH against the goal of improving population health status and equity worldwide allows careful consideration of the appropriateness and effectiveness of such efforts on their own and in concert with other interventions. Conclusions: The extent to which health equity is advanced should be the ultimate metric used to evaluate not only STEGH, but any global health endeavours.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".