The impact of training on self-reported performance in reproductive, maternal, and newborn health service delivery among healthcare workers in Tanzania: a baseline- and endline-survey
Bibliographic record
Abstract
BACKGROUND: Delivery of quality reproductive health services has been documented to depend on the availability of healthcare workers who are adequately supported with appropriate training. However, unmet training needs among healthcare workers in reproductive, maternal, and newborn health (RMNH) in low-income countries remain disproportionately high. This study investigated the effectiveness of training with onsite clinical mentorship towards self-reported performance in RMNH among healthcare workers in Mwanza Region, Tanzania. METHODS: The study used a quasi-experimental design with pre-and post-intervention evaluation strategy. The baseline was compared with two endline groups: those with intervention (training and onsite mentorship) and those without. The differences among the three groups in the sociodemographic characteristics were analyzed by using chi-square test for categorical variables, independent-sample t-test for continuous variables and Mann-Whitney U test for ordinal or skewed continuous data. The independent sample t-test was used to determine the effect of the intervention by comparing the computed self-reported performance on RMNH services between the intervention and control groups. The paired-samples t-test was used to measure the differences between before and after intervention groups. Significance was set at a 95% confidence interval with p ≤ 0.05. RESULTS: The study included a sample of 216 participants with before and after intervention groups comprising of 95 (44.0%) and 121 (56.0%) in the control group. The comparison between before and after intervention groups revealed a statistically significant difference (p ≤ 0.05) in all the dimensions of the self-reported performance scores. However, the comparison between intervention groups and controls indicated a statistical significant difference on intra-operative care (t = 3.10, df = 216, p = 0.002), leadership skills (t = 1.85, df = 216, p = 0.050), Comprehensive emergency obstetric and newborn care (CEMONC) (t = 34.35, df = 216, p ≤ 0.001), and overall self-reported performance in RMNH (t = 3.15, df = 216, p = 0.002). CONCLUSIONS: This study revealed that the training and onsite clinical mentorship to have significant positive changes in self-reported performance in a wide range of RMNH services especially on intra-operative care, leadership skills and CEMONC. However, further studies with rigorous designs are warranted to evaluate the long-term effect of such training programs on RMNH outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".