Acceptability and Potential Effectiveness of eHealth Tools for Training Primary Health Workers From Nigeria at Scale: Mixed Methods, Uncontrolled Before-and-After Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».