Implementation and User Evaluation of the SANGYAN Digital Health Platform to Enhance Knowledge About COVID-19 and Other Health Conditions: Quasi-Experimental Study
Bibliographic record
Abstract
Background: The spread of misinformation during the COVID-19 pandemic highlighted the importance of evidence-based information. The SANGYAN podcast promotes evidence-based knowledge on health-related issues in multiple languages in a simple, cost-effective, and concise manner. This provides individuals access to the appropriate information in an accessible manner. Objective: The study's goal is to assess user preferences for health information on a digital health platform designed to address COVID-19 misinformation. Methods: SANGYAN was developed by integrating the principles of social cognitive theory and information processing theory. The SANGYAN podcast was created to promote the importance of evidence-based information in order to address the spread of misinformation. The study design was a quasi-experimental study; prior to introducing the SANGYAN podcast, participants' sociodemographic information was collected, and health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine, Revised scale. After listening to the podcast, participants were interviewed about its usability, and they completed the System Usability Scale and the Client Satisfaction Questionnaire - 8. Data were collected from a total of 500 participants, 250 each from the Rural Health Training Center and Panimalar Medical College Hospital & Research Institute. The participants were older than 18 years when they were included. Descriptive and bivariate analyses were performed. Results: A total of 500 participants were enrolled in the study, 50% (250/500) from rural areas and 50% (250/500) from urban areas. The majority of the participants were 45 years to 64 years old (155/500, 31%), were women (289/500, 57.8%), had poor health literacy (384/500, 76.8%), and had a high school education or less than a high school certification (241/500, 48.2%). The mean overall System Usability System score was 70.9 (SD 17.73), with those aged 18 years to 24 years having the highest mean score (81.2, SD 15.48). High user satisfaction was present, with 97.6% (487/499) obtaining the desired information from the platform. Conclusions: The study revealed that the SANGYAN podcast provides information to diverse individuals, as it is multilingual, and was found useful by the participants.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".