Developing a tool to measure the reciprocal benefits that accrue to health professionals involved in global health
Bibliographic record
Abstract
Research to date on global health collaborations has typically focused on documenting improvements in the health outcomes of low/middle-income countries. Recent discourse has characterised these collaborations with the notion of 'reciprocal value', namely, that the benefits go beyond strengthening local health systems and that both partners have something to learn and gain from the relationship. We explored a method for assessing this reciprocal value by developing a robust framework for measuring changes in individual competencies resulting from participation in global health work. The validated survey and evidence-based framework were developed from a comprehensive review of the literature on global health competencies and reciprocal value. Statistical analysis including factor analysis, evaluation of internal consistency of domains and measurement of floor and ceiling effects were conducted to explore global health competencies among diverse health professionals at a tertiary paediatric health facility in Toronto, Canada. Factor analysis identified eight unique domains of competencies for health professionals and their institutions resulting from participation in global health work. Seven domains related to individual-level competencies and one emphasised institutional capacity strengthening. The resulting Global Health Competency Model and validated survey represent useful approaches to measuring the reciprocal value of global health work among diverse health professionals and settings. Insights gained through application of the model and survey may challenge the dominant belief that capacity strengthening for this work primarily benefits the recipient individuals and institutions in low/middle-income settings.
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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 | 0.040 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".