Utilizing Gamification, Artificial Intelligence, and mHealth for the Professional Development of Maternal Care Providers: Exploratory Pilot Cross-Sectional Study Assessing Providers' Satisfaction in Primary Health Care Centers in Lebanon
Notice bibliographique
Résumé
Background: High maternal morbidity and mortality rates globally, especially in low-income and lower-middle-income countries, highlight the critical role of skilled health care providers (HCPs) in preventing pregnancy-related complications among disadvantaged populations. Lebanon, hosting over 1.5 million refugees, is no exception. HCPs face significant challenges, including resource constraints and limited professional development opportunities, underscoring the need for continuous learning and innovative educational interventions. Artificial intelligence (AI) and gamification show promise in enhancing clinical performance and evidence-based practice. Objective: Considering the limited evidence on the effectiveness of integrating gamification and AI in a mobile app for professional development of HCPs providing maternal health services, this pilot study aims to assess the satisfaction and acceptability of HCPs with a novel mLearning tool, titled the "GAIN MHI" app (gamification, artificial intelligence, and mHealth network for maternal health improvement), at selected primary health care centers in Lebanon. Methods: This is a cross-sectional study that presents data collected from 12 participating HCPs, primarily obstetricians and midwives who have been using the GAIN MHI mobile app for professional development and learning. The survey used included Likert scale questions to assess HCPs' satisfaction, engagement, and evaluation of the gamification and AI components of the app. Open-ended questions gathered qualitative feedback on app preferences and potential improvements. Statistical analysis was performed to derive insights from the quantitative data collected. Subsequently, a descriptive analysis was performed, presenting the frequencies and percentages of various participant characteristics, as well as responses to the survey across all sections. Results: A total of 85% (n=10) of the HCPs, including midwives and doctors, were satisfied with the GAIN MHI mobile app, the user interface, and various content features. Engagement levels were robust (64.6%, SD 6.2%), notably impacting clinical routines and theoretical knowledge. The gamification and AI components garnered strong positive feedback, enhancing learning enjoyment (11/12, 92%). From a qualitative perspective, users expressed appreciation for the app's diverse content, user-friendliness, and motivation for continuous learning. Suggestions for expanding the content included a wide range of health topics, highlighting the app's potential applicability in various health care fields. Conclusions: HCPs, especially those practicing in underserved areas, face challenges in accessing professional development opportunities, highlighting the need for innovative pedagogical approaches using mobile technologies. This pilot study underlines the potential of using AI-based digital solutions for professional development with the aim of improving the quality of health services-in this case, maternal health services-through continuous learning and updates on the most recent evidence-based clinical guidelines. Future research should investigate the feasibility of applying similar solutions on a larger scale to reach a wider range of HCPs and cover other health topics. The applicability of such solutions in different contexts and low-resource settings should also be explored.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».