Community Health Worker Diabetes Prevention Awareness Training in an Immersive Virtual World Environment: Mixed Methods Pilot Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".