A Virtual Reality–Based mHealth App to Enhance Health Care Skills and Promote Health Equity by Empowering Health Care Professionals to Provide Empathetic, Compassionate, and Unbiased Patient Care Through Digital Experiential Learning: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Health care professionals' educational preparation and practices significantly influence care experiences and health outcomes. Deficient awareness of the impact of stereotypes, biases, prejudices, and social determinants of health (SDH) can lead to negative care experiences, strained health care professional-patient relationships, and health disparities. Addressing these challenges necessitates enhancing health care professionals' skills, including inclusive communication, cultural humility, recognition of SDH, and fostering empathy and compassion, promoting health equity and better care experiences. OBJECTIVE: This research aims to introduce a mobile health (mHealth) app designed using a digital experiential learning (DEL) approach to strengthen health care professionals' competencies, thereby improving patient care experiences and promoting health equity. The key objectives are to deliver essential health care skills, such as cultivating cultural humility, developing inclusive communication proficiencies, understanding the lasting impact of SDH, comprehending how implicit and explicit biases affect health outcomes, fostering compassionate and empathetic clinical attitudes, and promoting continuous professional development. Together, these aims advance patient-centered care and help reduce health disparities. METHODS: The mHealth app integrates virtual reality-based serious role-playing hypothetical scenarios and a life course module to provide health care professionals with immersive first-person learning experiences. The scenarios include a Syrian refugee with limited English proficiency and an African American pregnant woman with a history of opioid use disorder, each lasting ≈30 minutes to deliver essential health care skills. The pre- and postassessment questionnaires are integrated within the app to measure the learning outcomes of diverse professionals who voluntarily engaged with the app. Distinct hypotheses were formulated and evaluated, referring to specific questionnaire items to assess the app's impact on professionals' skills and attitudes. RESULTS: The mHealth app significantly enhanced health care professionals' skills and attitudes, including increased confidence, preparedness for patient interactions, awareness of the lasting impact of SDH, positive beliefs, reduced prejudice, improved perspectives on patient responsibility and external factors, as well as greater compassion and empathy. These outcomes, obtained through evaluating distinct hypotheses and analysis supported by multiple statistical approaches, including CI analysis, Cohen d effect sizes, odds ratios, 1-tailed paired t tests, and descriptive response distributions, directly align with the study's objectives, fostering health equity and patient-centered care. Overall, the app effectively improved health care professionals' competencies, contributing to better care experiences and outcomes while promoting health equity. CONCLUSIONS: This study delivers a comprehensive data-driven evaluation that validates the effectiveness of the mHealth app in enhancing health care professionals' skills and fostering health equity. It addresses challenges in health care education by delivering a scalable, accessible, and immersive learning platform. Supported by virtual reality technology and an experiential learning framework, the mHealth app presents a promising avenue to empower health care professionals with skills to provide patient-centered care in diverse, complex settings.
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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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».