Community Health Worker Diabetes Prevention Awareness Training in an Immersive Virtual World Environment: Mixed Methods Pilot Study
Notice bibliographique
Résumé
Background: The burden of diabetes and obesity are greater for some racial-ethnic minority groups in the United States, including non-Hispanic blacks, underscoring the importance of raising community awareness of diabetes prevention. Community health workers (CHWs) play a critical role in extending our reach into communities to raise awareness of diabetes prevention. Systematic training and support are central to their work. Remote approaches have been helpful in delivering training to overcome common participation barriers. One remote approach, immersive 3D virtual worlds (VW) offer a unique approach to providing remote training incorporating engaging interactive contextual learning opportunities. Objective: This study aimed to implement and evaluate an internet-based 3D VW model to remotely deliver an adapted CHW training program on diabetes prevention awareness for racial-ethnic minority communities. Methods: A sequential mixed methods design, including a pre-post pilot and explanatory phase, examined the feasibility, acceptability, and impact of the VW training. Female CHWs who self-identified as African American or Black or African Ancestry, between 21-65 years of age, fluent in English, and with risk factors for diabetes were recruited. CHW input was gathered to adapt a Centers for Disease Control and Prevention's CHW diabetes prevention awareness training and the VW environment for this study. The final adapted training was standardized for delivery over 10 weeks. Quantitative and qualitative data were collected to examine acceptability, feasibility, and impact of the training model. Primary quantitative pre-post outcomes included training content knowledge and confidence; and secondary behavioral outcomes included motivation for lifestyle change and eating habits. Focus group feedback was collected on acceptability and feasibility during the explanatory phase. Quantitative descriptive and qualitative thematic analysis approaches were used to examine the acceptability, feasibility, and impact of the VW training model. Results: A total of 26 CHWs initiated the study and 22 completed the postassessment. The majority of participants reported that their expectations were met across all sessions and content topics. Participants generally reported satisfaction with the information provided (20/22, 91% rated very good-excellent) and high levels of interactivity in the training (17/22, 77% rated very good-excellent). Results of the posttraining acceptability and feasibility quantitative survey and qualitative feedback were generally positive. Mean pre-post values improved across all quantitative outcomes for the VW training group (eg, 92% [11/12] improved in knowledge; 62% [8/13]-77% [10/13] improved across eating habits measures). Explanatory focus group findings were generally positive, highlighting satisfaction with the overall training, its interactivity, and content. The main constructive feedback was related to providing more training and support in using the avatar. Conclusions: Findings on the acceptability, feasibility, and preliminary impact of the VW training model are promising and support continued use, development, and research on this approach.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 0,006 |
| 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 | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».