Content and design of respectful maternity care training packages for health workers in sub‐Saharan Africa: Scoping review
Bibliographic record
Abstract
BACKGROUND: Training health workers might facilitate respectful maternity care (RMC); however, the content and design of RMC training remain unclear. OBJECTIVE: To explore the content and design of RMC training packages for health workers in sub-Saharan Africa. SEARCH STRATEGY: MEDLINE, EMBASE, CINAHL Complete, Web of Science Core Collections, SCOPUS, and grey literature sources (including websites of RMC-focused key organizations and Ministries of Health) were searched for journal papers, reports, and training guides from January 2006 up to August 2022. SELECTION CRITERIA: There were no restrictions on study designs, language, or health-worker cadre. Two reviewers independently screened results. DATA COLLECTION AND ANALYSIS: Key data, including training content and methods used, were extracted and summarized. MAIN RESULTS: Thirty-two citations from 26 studies/programs were identified (24 journal papers, 5 manuals/guides, 2 reports and 1 PhD thesis), with 27 citations from 22 studies informing the review findings. About half of all conducted studies were from East Africa. The most common topics in RMC trainings were communication, privacy and confidentiality, and human resources. Most trainings were multicomponent and appear to be largely in-service training. Health workers providing direct care to women, compared with non-clinical staff such as receptionists and cleaners, were the only recipients of training in most studies (81.8%). Two broad categories of training methods/tools were identified: workshop-based and action-based. Over 90% of the studies assessed impact of the training, with a majority focused on impacts on maternal health and care; however, half of the latter studies did not appear to have feedback mechanisms in place for implementing change. CONCLUSIONS: The content and design of RMC training in sub-Saharan Africa are multifaceted, suggesting the complexity of implementing/promoting RMC. Some progress has been made; however, missed opportunities in training remain with respect to study populations, training topics, cadres, and feedback mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".