Building Effective and Equitable Global Midwifery Collaborations: Research, Education, and Clinical Learning
Bibliographic record
Abstract
The historically unidirectional movement of global health ideas, practices, and protocols from the Global North (United States, Canada, European countries, Japan, South Korea, Taiwan, Australia, New Zealand, and Israel) to the Global South (Latin American countries, African countries, the Middle East excluding Israel and Asia countries, and Oceania excluding those previously mentioned) has displaced local practice and produced little sustainable change. Actively addressing these unintended consequences, practitioners at Yale School of Nursing in the United States formed a sustainable, mutually beneficial partnership with Makerere University College of Health Sciences and Mother Health International community birth center in Atiak, Uganda, to reduce perinatal mortality in areas with the highest burden. Goals included establishing a collaborative midwifery education and research partnership; developing an interprofessional clinical rotation; and developing a blueprint for teaching the midwifery model of care in the Global South. The partnership has successfully produced outputs including midwifery education support, research, clinical training, interprofessional capacity building, and community integration within local health care systems. Lessons learned from program design, implementation, and evaluation can inform global learning collaborations that are multidirectional and lead to more equitable midwifery collaborations.
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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 | 0.111 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".