The impact of training on perceived performance in reproductive, maternal, and newborn health service delivery among healthcare workers in Tanzania: A baseline- and endline-survey
Bibliographic record
Abstract
Abstract BackgroundDelivery 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 trainings with onsite clinical mentorship towards perceived importance and performance in RMNH among healthcare workers in Mwanza Region of Tanzania.MethodsThe study used a quasi-experimental design using single group pre-and post-intervention evaluation strategy. The training needs of healthcare workers from the selected health facilities were assessed, skills gaps identified and ranked according to priority. Training courses that addressed skills gaps were developed and delivered with adaptations of the national guidelines followed by onsite clinical mentorship for one year. The baseline and endline survey were conducted at 3 years interval to assess change in HCWs on their perceived importance and performance on different aspects of RMNH care. Independent samples t-tests were used to compare differences in perceived performance in selected training areas between baseline and endline. Significance was set at p < 0.05.ResultsTNA was administered to 152 and 216 healthcare workers at baseline and endline respectively. In total, 141 (65%) of the 216 end line survey participants had received at least one IMPACT project training course and at least three mentorship visits. Participants were matched on their age and duration in RMNH services, but differed in age and duration of employment. Comparison between baseline and endline by using the training needs analysis questionnaire scores showed statistically significant positive changes (p ≤ .05) in most training needs analysis items, except for some items including those related to research capacity and provision of health education for cancer.ConclusionsThe findings revealed that the training and onsite clinical mentorship program that address the actual needs of healthcare workers to have significant positive changes in perceived performance in a wide range of RMNH services. 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".