Assessment of Audio‐Visual and Print Educational Material using PEMAT for Prevention of Dementia: A SMRUTHI, India Initiative
Bibliographic record
Abstract
BACKGROUND: SMRUTHI-India is a multimodal initiative to establish a cohort across four sites in India for randomized controlled interventions to prevent dementia in the at-risk elderly population. Dementia prevalence in India is 7.9%, with higher rates in rural than urban areas. Given the high prevalence, low health literacy, and multicultural context, developing relevant print and audiovisual materials for psycho-education tailored to rural populations is essential. METHOD: The Care Bundle Module (CBM) was developed by a multidisciplinary team at AIIMS, New Delhi focused on the prevention of Dementia in rural population. The CBM Booklet was created and Content Validity Index (CVI) was also calculated based on feedback from five experts. It was then transformed into 10 animated videos in three stages: Pre-Production, Production and Post-Production. To evaluate "understandability" and "actionability" of both the booklet and videos, the Patient Educational Material Assessment Tool (PEMAT) for print (P) and audio-visual (AV) formats was used among 83 (P) and 86 (AV) participants across the sites. Qualitative feedback from the target population and a behavioral expert was also taken and is currently being incorporated into the material. RESULT: The CBM Booklet's S-CVI/Avg was 0.933, indicating 93.3% content validity based on expert ratings. PEMAT understandability scores averaged 96.35% (Print) and 95.46% (Audio-Visual), while actionability scores were 97.90% (P) and 98.75% (AV). PEMAT-based CVI showed strong regional agreement: Himachal Pradesh reported S-CVI/Avg of 0.9802 (P) and 0.993 (AV), Kappa 0.98 and 0.993; Karnataka 0.943 (P) and 0.975 (AV), Kappa 0.94 and 0.97; Rajasthan 0.9828 (P) and 0.9791 (AV), Kappa 0.9829 and 0.979; Tripura 0.9921 (P) and 0.9053 (AV), Kappa 0.9921 and 0.902. These results show high I-CVI and Kappa across regions, confirming excellent content validity and inter-rater agreement. CONCLUSION: The CBM materials demonstrated strong agreement in both experts and target population as reflected in high S-CVI/Avg and Kappa values across regions. PEMAT scores confirmed excellent understandability and actionability in both print and audiovisual formats. These results highlight the relevance and effectiveness of the CBM in delivering dementia-related psychoeducation in rural India.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".