Healthcare Providers' Experiences With a Clinical Mentorship Intervention to Improve Reproductive, Maternal and Newborn Care in Mwanza, Tanzania
Bibliographic record
Abstract
Introduction: There is increasing evidence suggesting that clinical mentorship (CM) involving on-the-job training is one of the critical resources-friendly entry points for strengthening the knowledge and skills of healthcare providers (HCPs), which in turn facilitate the delivery of effective reproductive, maternal, and newborn health (RMNH) care. The article explores the experiences of HCPs following participation in the CM program for RMNH in eight districts of Mwanza Region in Tanzania. Materials and Methods: A qualitative descriptive design employing data from midterm project review meetings and Key Informant Interviews (KIIs) with purposefully selected HCPs (mentors and mentees) and District Medical Officers (DMOs) during endline evaluation were employed. Interview data were managed using Nvivo Software and analyzed thematically. Results: A total of 42 clinical mentors and master mentors responded to a questionnaire during the midterm review meeting. Then, a total of 17 KIIs were conducted with Mentees (8), Mentors (5), and DMOs (4) during endline evaluation. Five key themes emerged from participants' accounts: (i) the topics covered during CM visits; (ii) the benefits of CM; (iii) the challenges of CM; (iv) the drivers of CM sustainability; and (iv) suggestions for CM improvement. The topics of CM covered during visits included antenatal care, neonatal resuscitation, pregnancy monitoring, management of delivery complications, and infection control and prevention. The benefits of CM included increased knowledge, skills, confidence, and change in HCP's attitude and increased client service uptake, quality, and efficiency. The challenges of CM included inadequate equipment for learning and practice, the limited financial incentive to mentees, shortage of staff and time constraints, and weaker support from management. The drivers of CM sustainability included the willingness of mentees to continue with clinical practice, ongoing peer-to-peer mentorship, and integration of the mentorship program into district health plans. Finally, the suggestions for CM improvement included refresher training for mentors, engagement of more senior mentors, and extending mentorship beyond IMPACT catchment facilities. Conclusion: CM program appears to be a promising entry point to improving competence among HCPs and the quality and efficiency of RMNH services potentially contributing to the reduction of maternal and neonatal deaths. Addressing the challenges cited by participants, particularly the equipment for peer learning and practice, may increase the success of the CM program.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".