Training Needs Assessment of Health Care Professionals in Reproductive, Maternal and Newborn Health in a Low-Income Setting in Tanzania
Bibliographic record
Abstract
Abstract BackgroundHealthcare delivery globally and particularly in low-income setting is challenged by multiple, complex and dynamic problems. The reproductive, maternal and newborn health (RMNH) care is among the most affected areas resulting into high maternal and neonatal mortality and morbidity across the Sub Saharan region and Tanzania in particular. However, under-investment in adequate education and training of health care providers (HCPs) is reported worldwide and contributes to the critical shortages, and lack of adequate knowledge and skills among HCPs. The aim of this study was to assess the training needs among HCPs of RMNH care in selected health facilities of Mwanza, Tanzania. MethodsA cross-sectional descriptive and analytic survey using a self- administered questionnaire was conducted in 36 out of 80 health care facilities included in the Government of Canada and Aga Khan Foundation Canada funded Improving Access to Reproductive, Maternal and Newborn Health in Mwanza, Tanzania (IMPACT) project within the 8 Councils of Mwanza region in Tanzania. The training needs assessment (TNA) tool adapted from the Hennessy-Hicks’ Training Needs Assessment Questionnaire (TNAQ) instrument was used for data collection. The HCPs provided ratings on the importance of their task and their current performance of the task. The differences in scores were calculated to identify the greatest training needs. ResultsOut of 153 HCPs who responded to the TNA questionnaire, majority were registered (n=62) and enrolled (n=43) nurses. Ninety percent (n= 137) were from government-owned health facilities, mostly from hospitals 68 (45%). Training needs were high in 16 areas (out of 49) including cervical cancer screening and care; accessing research resources; basic and comprehensive emergency obstetric and newborn care; and sexual and gender-based violence. The overall perceived importance of the training needs was significantly associated with perceived performance of tasks related to RMNH services (Pearson Correlation (r) = .256; p <001).ConclusionsThe study highlights 16 (out of 49) training gaps as perceived by HCPs working in RMNH in Tanzania. The utilization of findings from the TNA has great potential to facilitate designing of effective trainings for local RMNH services delivery hence improve the overall quality of care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".