Visiting Trainees in Global Settings: Host and Partner Perspectives on Desirable Competencies
Bibliographic record
Abstract
BACKGROUND: Current competencies in global health education largely reflect perspectives from high-income countries (HICs). Consequently, there has been underrepresentation of the voices and perspectives of partners in low- and middle-income countries (LMICs) who supervise and mentor trainees engaged in short-term experiences in global health (STEGH). OBJECTIVE: The objective of this study was to better understand the competencies and learning objectives that are considered a priority from the perspective of partners in LMICs. METHODS: A review of current interprofessional global health competencies was performed to design a web-based survey instrument in English and Spanish. Survey data were collected from a global convenience sample. Data underwent descriptive statistical analysis and logistic regression. FINDINGS: The survey was completed by 170 individuals; 132 in English and 38 in Spanish. More than 85% of respondents rated cultural awareness and respectful conduct while on a STEGH as important. None of the respondents said trainees arrive as independent practitioners to fill health care gaps. Of 109 respondents, 65 (60%) reported that trainees gaining fluency in the local language was not important. CONCLUSIONS: This study found different levels of agreement between partners across economic regions of the world when compared with existing global health competencies. By gaining insight into host partners' perceptions of desired competencies, global health education programs in LMICs can be more collaboratively and ethically designed to meet the priorities, needs, and expectations of those stakeholders. This study begins to shift the paradigm of global health education program design by encouraging North-South/East-West shared agenda setting, mutual respect, empowerment, and true collaboration.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".