Developing and implementing a novel mentorship model (4+ 1) for maternal, newborn and child health in Rwanda
Bibliographic record
Abstract
Abstract Background There are a number of factors that may contribute to high mortality and morbidity of women and newborns in low-income countries. These include a shortage of competent health care providers (HCP) and a lack of sufficient continuous professional development (CPD) opportunities. Strengthening the skills and building the capacity of HCP involved in the provision of maternal, newborn and child health (MNCH) is essential to ensure quality care for mothers, newborns and children. To address this challenge in Rwanda, mentorship of HCPs was identified as an approach that could help build capacity, improve the provision of care and accelerate the reduction in maternal and neonatal mortality and morbidity. In this paper, we describe the development and implementation of a novel mentorship model named Four plus One (4+ 1) for MNCH in Rwanda. Methods The mentorship model built on the basis of inter-professional collaboration (IPC) was developed in early 2017 through consultations with different key actors. The design phase included refresher courses in specific skills and training course on mentoring. Field visits were conducted in 10 hospitals from June 2017 to February 2020. Hospital management teams (MT) were involved in the development and implementation of this mentorship model to ensure ownership of the program. Results Upon completion of planned visits to each hospital, a total of 218 HCPs were involved in the process. Reports prepared by mentors upon each mentorship visit and compiled by Training Support and Access Model (TSAM) for MNCH’CPD team, highlighted the mothers and newborns who were saved by both mentors and mentees. Also, different logbooks of mentees showed how the capacity of staff was strengthened, thereby suggesting effectiveness of the model. Through different mentorship coordination meetings, the model was much appreciated by the MTs of hospitals, especially the IPC component of the model and confirmed the program ‘effectiveness. Conclusion The initiation of a mentorship model built on IPC together with the involvement of the leadership of the hospital may be the cause effect of reduction of specific mortality and improve MNCH in low resource settings even when there are a limited number of specialists in the health facilities.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| 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".