Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables
Bibliographic record
Abstract
OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear. DESIGN: We explored numerous psychopathology, sociodemographic, and virus exposure-related variables associated with anxiety and perceived threat of death regarding COVID-19. METHOD: We recruited 908 adults from Eastern China for a cross-sectional web survey, from 24 February to 15 March 2020, when social distancing was heavily enforced in China. We used several machine learning algorithms to train our statistical model of predictor variables in modeling COVID-19-related anxiety, and perceived threat of death, separately. We trained the model using many simulated replications on a random subset of participants, and subsequently externally tested on the remaining subset of participants. RESULTS: Shrinkage machine learning algorithms performed best, indicating that stress and rumination were the most important variables in modeling COVID-19-related anxiety severity. Health anxiety was the most potent predictor of perceived threat of death from COVID-19. CONCLUSIONS: Results are discussed in the context of research on anxiety and fear from prior virus outbreaks, and from theory on outbreak-related emotional vulnerability. Implications regarding COVID-19-related anxiety are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".