Acceptability and Potential Effectiveness of eHealth Tools for Training Primary Health Workers From Nigeria at Scale: Mixed Methods, Uncontrolled Before-and-After Study
Bibliographic record
Abstract
BACKGROUND: The in-service training of frontline health workers (FHWs) in primary health care facilities plays an important role in improving the standard of health care delivery. However, it is often expensive and requires FHWs to leave their posts in rural areas to attend courses in urban centers. This study reports the implementation of a digital health tool for providing video training (VTR) on maternal, newborn, and child health (MNCH) care to provide in-service training at scale without interrupting health services. The VTR intervention was supported by satellite communications technology and existing 3G mobile networks. OBJECTIVE: This study aims to determine the feasibility and acceptability of these digital health tools and their potential effectiveness in improving clinical knowledge, attitudes, and practices related to MNCH care. METHODS: A mixed methods design, including an uncontrolled pre- and postquantitative evaluation, was adopted. From October 2017 to May 2018, a VTR mobile intervention was delivered to FHWs in 3 states of Nigeria. We examined changes in workers' knowledge and confidence in delivering MNCH services through a pre- and posttest survey. Stakeholders' experiences with the intervention were explored through semistructured interviews that drew on the technology acceptance model to frame contextual factors that shaped the intervention's acceptability and usability in the work environment. RESULTS: In total, 328 FHWs completed both pre- and posttests. FHWs achieved a mean pretest score of 51% (95% CI 48%-54%) and mean posttest score of 69% (95% CI 66%-72%), reflecting, after adjusting for key covariates, a mean increase between the pre- and posttest of 17 percentage points (95% CI 15-19; P<.001). Variation was identified in pre- and posttest scores by the sex and location of participants alongside topic-specific areas where scores were lowest. Stakeholder interviews suggested a wide acceptance of VTR Mobile (delivered via digital technology) as an important tool for enhancing the quality of training, reinforcing knowledge, and improving health outcomes. CONCLUSIONS: This study found that VTR supported through a digital technology approach is a feasible and acceptable approach for supporting improvements in clinical knowledge, attitudes, and reported practices in MNCH. The determinants of technology acceptance included ease of use, perceived usefulness, access to technology and training contents, and the cost-effectiveness of VTR, whereas barriers to the adoption of VTR were poor electricity supply, poor internet connection, and FHWs' workload. The evaluation also identified the mechanisms of the impact of delivering VTR Mobile at scale on the micro (individual), meso (organizational), and macro (policy) levels of the health system. Future research is required to explore the translation of this digital health approach for the VTR of FHWs and its impact across low-resource settings to ameliorate the financial and time costs of training and support high-quality MNCH care delivery. TRIAL REGISTRATION: ISRCTN Registry 32105372; https://www.isrctn.com/ISRCTN32105372.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".