Attitudes toward COVID-19 vaccination and willingness to pay: comparison of people with and without mental disorders in China
Bibliographic record
Abstract
Background Acceptance and willingness to pay for the COVID-19 vaccine are unknown. Aims We compared attitudes toward COVID-19 vaccination in people suffering from depression or anxiety disorder and people without mental disorders, and their willingness to pay for it. Method Adults with depression or anxiety disorder (n = 79) and healthy controls (n = 134) living in Chongqing, China, completed a cross-sectional study between 13 and 26 January 2021. We used a validated survey to assess eight aspects related to attitudes toward the COVID-19 vaccines. Psychiatric symptoms were assessed by the 21-item Depression, Anxiety and Stress Scale. Results Seventy-six people with depression or anxiety disorder (96.2%) and 134 healthy controls (100%) reported willingness to receive the COVID-19 vaccine. A significantly higher proportion of people with depression or anxiety disorder (64.5%) were more willing to pay for the COVID-19 vaccine than healthy controls (38.1%) (P ≤ 0.001). After multivariate adjustment, severity of depression and anxiety was significantly associated with willingness to pay for COVID-19 vaccination among psychiatric patients (P = 0.048). Non-healthcare workers (P = 0.039), health insurance (P = 0.003), living with children (P = 0.006) and internalised stigma (P = 0.002) were significant factors associated with willingness to pay for COVID-19 vaccine in healthy controls. Conclusions To conclude, psychiatric patients in Chongqing, China, showed high acceptance and willingness to pay for the COVID-19 vaccine. Factors associated with willingness to pay for the COVID-19 vaccine differed between psychiatric patients and healthy controls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".