Predicting Covid-19 Preventive Healthy Behaviors Based on Dysfunctional Attitudes in Five Countries
Bibliographic record
Abstract
Background: Dysfunctional attitudes are biased assumptions and beliefs that the subject has toward himself, his surroundings, and the future world. Objectives: The present study aimed to predict COVID-19 preventive healthy behaviors based on dysfunctional attitudes in five countries. Methods: This was a descriptive, correlational study, and the statistical population of the study included all individuals over the age of 18 years residing in Iran, Australia, England, Sweden, and Canada. Subjects were selected by the voluntary sampling method in the Spring of 2020. In total, 498 electronic questionnaires encompassing three sections of demographic characteristics, dysfunctional attitude scale (1987), and COVID-19 preventive health behaviors questionnaire (2020) were completed online. In addition, data analysis was performed in SPSS version 21 using Pearson’s correlation coefficient and stepwise multiple regression. Results: In this study, there was a significant negative relationship between dysfunctional attitudes and COVID-19 preventive healthy behaviors (P < 0.001). In addition, perfectionism, gender, and age predicted healthy behaviors (P < 0/001). The results of the comparison of Iran with other countries also demonstrated a significant reverse correlation between dysfunctional attitudes and healthy behaviors (P < 0.001). Moreover, there was a significant association between marital status, age, level of education, gender (P < 0.001), and economic status (P < 0.05) with healthy behaviors in Iran while no significant relationship was observed in other countries studied in this regard. Conclusions: It is suggested that workshops on changing dysfunctional attitudes and strengthening positive attitudes in community members be held in-person or via cyberspace before or during the occurrence of crises such as the COVID-19 outbreak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".