Prediction of Self-care Behaviors in COVID-19 Prevention Based on Persuasion Techniques
Bibliographic record
Abstract
Background: Persuasion is a method used to correct and modify the attitude and behaviors of community members to protect collective benefits, especially during crises. Objectives: The present study aimed to predict COVID-19 preventive behaviors based on persuasion techniques in five countries. Methods: This descriptive, correlational study was conducted on the population aged more than 18 years in Iran, Australia, the United Kingdom, Sweden, and Canada. The sample size determined by Morgan’s table was 498 individuals who were selected via convenience sampling in the spring of 2020. Data were collected online using a Demographic Questionnaire, a Persuasion Scale (2020), and the Questionnaire of Healthy Preventive Behaviors for COVID-19 (2020). The inclusion criteria were the age of more than 18 years and basic literacy, and the exclusion criterion was incomplete questionnaires. Data analysis was performed in SPSS version 21 using Pearson’s correlation-coefficient and multiple regression analysis. Results: A positive significant correlation was observed between persuasion techniques and healthy preventive behaviors for COVID-19 (P < 0.001). Among the components of persuasion, fear, interest in the messenger, frequency of the message, and reliability of the messenger could most significantly predict healthy behaviors (P < 0.001). Conclusions: According to the results, the mass media and authorities could enhance the effectiveness of their agenda by identifying the influential factors in the success of persuasion techniques. These findings could be beneficial to social psychiatrists, authorities, and the mass media.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".