Psychosocial Predictors and Mediators Relating to the Preventive Behaviors of Hospital Workers During the COVID-19 Pandemic in Turkey
Bibliographic record
Abstract
OBJECTIVE: The aim is to analyze the relation of psychosocial factors to COVID-19 contraction, vaccination, and preventive health behavior in and outside work. METHODS: The questionnaire data from hospital-workers in Turkey is analyzed using independent-samples t-test, logistic regression, linear regression, and mediation analyses. We developed a questionnaire on mask-hygiene-distance measures and also used previously developed scales including the Big Five Personality Questionnaire, Young Schema Questionnaire Short Form 3, Schwartz's Basic Human Values Scale, Short Dark Triad. RESULTS: The odds of being infected by COVID-19 increases by self-direction. The odds of being vaccinated increases by age and conformity, and decreases by emotional stability. Education predicts certain preventive behaviors at work negatively and outside work positively. Older age, being a woman, having chronic disease, the self-transcendence and conservation values, agreeableness, and conscientiousness predict more preventive behaviors. The self-enhancement and openness to change values, the Dark Triad, and early maladaptive schemas predict more risky behaviors. CONCLUSIONS: Designing prohealth policies requires further elaboration on the relation of psychosocial factors to preventive behaviors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".