Developing and implementing a model of equitable distribution of mentorship in districts with spatial inequities and maldistribution of human resources for maternal and newborn care in Rwanda
Bibliographic record
Abstract
BACKGROUND: The shortage of health care providers (HCPs) and inequity in their distribution along with the lack of sufficient and equal professional development opportunities in low-income countries contribute to the high mortality and morbidity of women and newborns. Strengthening skills and building the capacity of all HCPs involved in Maternal and Newborn Health (MNH) is essential to ensuring that mothers and newborns receive the required care in the period around birth. The Training, Support, and Access Model (TSAM) project identified onsite mentorship at primary care Health Centers (HCs) as an approach that could help reduce mortality and morbidity through capacity building of HCPs in Rwanda. This paper presents the results and lessons learnt through the design and implementation of a mentorship model and highlights some implications for future research. METHODS: The design phase started with an assessment of the status of training in HCs to inform the selection of Hospital-Based Mentors (HBMs). These HBMs took different courses to become mentors. A clear process was established for engaging all stakeholders and to ensure ownership of the model. Then the HBMs conducted monthly visits to all 68 TSAM assigned HCs for 18 months and were extended later in 43 HCs of South. Upon completion of 6 visits, mentees were requested to assist their peers who are not participating in the mentoring programme through a process of peer mentoring to ensure sustainability after the project ends. RESULTS: The onsite mentorship in HCs by the HBMs led to equal training of HCPs across all HCs regardless of the location of the HC. Research on this mentorship showed that the training improved the knowledge and self-efficacy of HCPs in managing postpartum haemorrhage (PPH) and newborn resuscitation. The lessons learned include that well trained midwives can conduct successful mentorships at lower levels in the healthcare system. The key challenge was the inconsistency of mentees due to a shortage of HCPs at the HC level. CONCLUSIONS: The initiation of onsite mentorship in HCs by HBMs with the support of the district health leaders resulted in consistent and equal mentoring at all HCs including those located in remote areas.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".