Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: During the coronavirus disease (COVID-19) pandemic, social media platforms have become active sites for the dissemination of conspiracy theories that provide alternative explanations of the cause of the pandemic, such as secret plots by powerful and malicious groups. However, the association of individuals' beliefs in conspiracy theories about COVID-19 with mental health and well-being issues has not been investigated. This association creates an assessable channel to identify and provide assistance to people with mental health and well-being issues during the pandemic. OBJECTIVE: Our aim was to provide the first evidence that belief in conspiracy theories regarding the COVID-19 pandemic is a predictor of the mental health and well-being of health care workers. METHODS: We conducted a survey of 252 health care workers in Ecuador from April 10 to May 2, 2020. We analyzed the data regarding distress and anxiety caseness with logistic regression and the data regarding life and job satisfaction with linear regression. RESULTS: Among the 252 sampled health care workers in Ecuador, 61 (24.2%) believed that the virus was developed intentionally in a lab; 82 (32.5%) experienced psychological distress, and 71 (28.2%) had anxiety disorder. Compared to health care workers who were not sure where the virus originated, those who believed the virus was developed intentionally in a lab were more likely to report psychological distress and anxiety disorder and to have lower levels of job satisfaction and life satisfaction. CONCLUSIONS: This paper identifies belief in COVID-19 conspiracy theories as an important predictor of distress, anxiety, and job and life satisfaction among health care workers. This finding will enable mental health services to better target and provide help to mentally vulnerable health care workers during the ongoing COVID-19 pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".