Does bundling reminders with messages debunking misconceptions improve the demand for preventive health services? A randomized controlled trial among adults with hypertension in Punjab, India
Bibliographic record
Abstract
Regular follow-up visits are a crucial component of prevention and care for several important non-communicable diseases (NCDs). Yet evidence across low- and middle-income countries (LMICs) reveals low demand for preventive healthcare visits. While reminders are commonly used to improve follow-up visit attendance, we hypothesized that low demand could also be driven by misconceptions about the need for preventive care. We thus conducted a randomized evaluation of an enhanced reminder intervention that combined a traditional reminder with debunking information aimed at correcting misconceptions around preventive healthcare. We focused specifically on correcting misconceptions about and improving follow-up visit attendance for hypertension among a sample of 463 individuals with uncontrolled blood pressure recruited from two public hospitals in Punjab, India. Our enhanced reminder was highly effective and improved follow-up visit attendance by 12.1 percentage points. Importantly, we found widespread misconceptions about when hypertension care and treatment are needed among participants at baseline. However, our enhanced reminder did not significantly correct these misconceptions, suggesting that the reminder's effect was primarily mediated through its effect on salience rather than belief correction. While our reminder improved preventive care seeking, the results reveal the challenge of changing deeply rooted misconceptions and suggest that there is still significant scope for further improving demand by combining reminders with more effective belief correction strategies. • Extremely low follow-up attendance among individuals with uncontrolled blood pressure. • Participants did have widespread misconceptions around hypertension care. • Our enhanced reminder that combined debunking significantly improved attendance. • However, our debunking did not change misconceptions highlighting sticky beliefs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".