Global collaboration between Tanzania and Japan to advance midwifery profession: A case report of a partnership model
Bibliographic record
Abstract
The global health agenda to reduce maternal mortality is delayed in Sub-Saharan Africa. The shortage of skilled birth attendants in Tanzania hinders the improvement of midwifery care to prevent maternal mortality and morbidity. It is urgently neccesary to develop midwifery leaders capable of working as educators, researchers, administrators, and advanced practitioners, contributing to the improvement of midwifery care and maternal child health in their own country. This report describes the process of establishing the first midwifery master’s program in Tanzania through the efforts of two academic institutions, one in Tanzania and one in Japan. The collaboration developed a sustainable partnership model for the advancement of midwifery education. This partnership model was based upon the professional relationships corresponding with our values of humanized childbirth and people-centered care. The key elements for the project success included: (1) spending adequate time for in-person communication with the collaborative partner; (2) sharing the same goals and concepts; (3) understanding different values and norms for working and living; (4) learning ways of communication and project implementation in the partner's culture and (5) confirming the feasibility, which could increase team members’ motivation and commitment. Midwives from the two institutions both gained knowledge and research outcomes as well as the satisfaction of establishing the midwifery master’s program. To improve the remaining global maternal health issues, this win-win collaboration should be considered as the 21st century’s partnership model for the global health community.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".