An mHealth-Based Health Management Information System Among Health Workers in Volta and Eastern Regions of Ghana: Pre-Post Comparison Analysis
Notice bibliographique
Résumé
BACKGROUND: Despite the increasing attention to electronic health management information systems (HMISs) in global health, most African countries still depend on inefficient paper-based systems. Good Neighbors International and Evaluate 4 Health have recently supported the Ghana Health Service on the rollout of a mobile health-based HMIS called the e-Tracker system in 2 regions in Ghana. The e-Tracker is an Android-based tracker capture app that electronically manages maternal and child health (MCH) data. The Ghana Health Service has implemented this new system in Community Health Planning and Services in the 2 regions (Volta and Eastern). OBJECTIVE: This study aims to evaluate changes in health workers' capacity and behavior after using the e-Tracker to deliver MCH services. Specifically, the study assesses the changes in knowledge, attitude, and practice (KAP) of the health workers toward the e-Tracker system by comparing the pre- and postsurvey results. METHODS: The KAP of frontline health workers was measured through self-administered surveys before and after using the e-Tracker system to assess their capacity and behavioral change toward the system. A total of 1124 health workers from the Volta and Eastern regions responded to the pre-post surveys. This study conducted the McNemar chi-square test and Wilcoxon signed-rank test for a pre-post comparison analysis. In addition, random-effects ordered logistic regression analysis and random-effects panel analysis were conducted to identify factors associated with KAP level. RESULTS: The pre-post comparison analysis showed significant improvement in health workers' capacity, with higher knowledge and practice levels after using the e-Tracker system. As for knowledge, there was a 9.9%-point increase (from 559/1109, 50.41% to 669/1109, 60.32%) in the proportion of the respondents who were able to generate basic statistics on the number of children born in a random month within 30 minutes. In the practice section, the percentage of respondents who had scheduled clientencounters increased from 91.41% (968/1059) to 97.83% (1036/1059). By contrast, responses to the attitude (acceptability) became less favorable after experiencing the actual system. For instance, 48.53% (544/1121) initially expressed their preferences for an electronic system; however, the proportion decreased to 33.45% (375/1121) after the intervention. Random-effects ordered logistic regression showed that days of overwork were significantly associated with health workers' attitudes toward the e-Tracker system. CONCLUSIONS: This study provides empirical evidence that the e-Tracker system is conducive to enhancing capacity in MCH data management for providing necessary MCH services. However, the change in attitude implies that the users appear to feel less comfortable using the new system. As Ghana plans to scale up the electronic HMIS system using the e-Tracker to the national level, strategies to enhance health workers' attitudes are necessary to sustain this new system.
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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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».