Mentors’ perspectives on strengths and weaknesses of a novel clinical mentorship programme in Rwanda: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: To identify mentors' perspectives on strengths and weaknesses of the Training, Support and Access Model for Maternal, Newborn and Child Health (TSAM-MNCH) clinical mentorship programme in Rwandan district hospitals. Understanding the perspectives of mentors involved in this programme can aid in the improvement of its implementation. DESIGN: The study used a qualitative approach with in-depth interviews. SETTING: Mentors of TSAM-MNCH clinical mentorship programme mentoring health professionals at district hospitals of Rwanda. PARTICIPANTS: 14 TSAM mentors who had at least completed six mentorship visits on a regular basis in three selected district hospitals. RESULTS: Mentors' accounts demonstrated an appreciation of the two mentoring structures which are interprofessional collaboration and training. These structures are highlighted as the strengths of the mentoring programme and they play a significant role in the successful implementation of the mentorship model. Inconsistency of mentoring activities and lack of resources emerged as major weaknesses of the clinical mentorship programme which could hinder the effectiveness of the mentoring scheme. CONCLUSION: The findings of this study highlight the strengths and weaknesses perceived by mentors of the TSAM-MNCH clinical mentorship programme, providing insights that can be used to improve its implementation. The study represents unique TSAM-MNCH structural settings, but its findings shed light on Rwandan health system issues that need to be further addressed to ensure better quality of care for mothers, newborns and children.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".