Applying equity-centered principles in an interprofessional global health course: A mixed methods study
Bibliographic record
Abstract
Abstract Background Medical students, practitioners and other health professionals are commonly unprepared to address the many complex issues that emerge while conducting research in the Global South. As a response to identified deficiencies in global health education, a hybrid online/face-to-face multi-institutional credit course was developed based on the principles advanced by the Canadian Coalition for Global Health Research (CCGHR), namely Authentic partnering, Inclusion, Shared benefits, Commitment to the future, Responsiveness to causes of inequities, and Humility. This study aimed to analyze the extent to which the course was effective in fortifying attitudes consistent with the CCGHR principles; identify successes and challenges; and assess how a course such as this can fill an identified gap. Methods This interprofessional course was offered to 25 graduate and postgraduate students in various health professions and public health. Faculty were drawn from medicine, public health, nursing and social sciences from four universities in Western Canada. A pre-post retrospective survey, key informant interviews and participant observation were used to gather data for this study. Results Findings showed that student attitudes regarding global health research and practice significantly evolved towards views consistent with the principles articulated. The multiple instructors and hybrid course format created both opportunities and challenges; the interprofessional nature of the cohort was considered a strong asset, as was the fact that many students came from the Global South. Some students suggested that the course could be further strengthened by concretely partnering with institutions in the Global South rather than offered solely to learners registered in universities in the Global North. Conclusions While weaknesses were identified, results support the conclusion that a course focused on the CCGHR principles could be useful in preparing the next generation of global health researchers and practitioners to mitigate historical limitations in this field. Longitudinal follow-up is warranted to provide more definitive conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".