Effects of a Planned Web-Based Educational Intervention Based on the Health Belief Model for Patients With Ischemic Stroke in Promoting Secondary Prevention During the COVID-19 Lockdown in China: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Some common modified vascular risk factors remain poorly controlled among stroke survivors, and educational programs may help improve these conditions. Objective: This study aimed to evaluate the effect of a planned web-based educational intervention based on the health belief model (HBM) in promoting secondary prevention among patients with ischemic stroke. Methods: An evaluation-blinded quasi-experimental trial with a historical control group was conducted. Patients admitted from March to June 2020 were assigned to the historical control group, and patients admitted from July to October 2020 were assigned to the intervention group. The control group received routine health management. The intervention group received 6 additional sessions based on the HBM via Tencent Meeting, an audio and video conferencing application, within 3 months after discharge. Sessions were held every 2 weeks, with each session lasting approximately 40 minutes. These sessions were conducted in small groups, with about 8 to 10 people in each group. The primary outcomes were changes in blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), and the proportion of patients achieving the treatment target. The secondary outcomes were medication adherence, assessed with the Morisky Medicine Adherence Scale (MMAS), and disability, assessed with the modified Rankin scale. Results: In total, 315 patients experiencing their first-ever stroke were analyzed. More patients in the intervention group had controlled BP (41.9% vs 28.4%; adjusted odds ratio [aOR] 1.93; P=.01), LDL-C (83.1% vs 67.7%; aOR 2.66; P=.001), and HbA1c (91.9% vs 83.9%; aOR: 3.37; P=.04) levels as well as a significant postintervention decrease in the systolic BP (adjusted β -3.94; P=.02), LDL-C (adjusted β -0.21; P=.008), and HbA1c (adjusted β -0.27; P<.001), compared with control groups. Significant between-group differences were observed in medication adherence (79.4% vs 63.2%; aOR 2.31; P=.002) but not in favorable functional outcomes. Conclusions: A web-based education program based on the HBM may be more effective than current methods used to educate patients having strokes on optimal vascular risk factors and medication adherence.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".