Examining Risk and Protective Factors for Psychological Health during the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has impacted the lives of people globally, and the significant mental health consequences of this pandemic are beginning to be documented. In addition to sociodemographic and COVID-19 specific factors, psychological risk and protective mechanisms likely influence individual differences in mental health symptoms in the context of the COVID-19 pandemic. We examined associations between a broad set of risk and protective factors with symptoms of depression, anxiety, alcohol problems, and eating pathology, and investigated interactions between objective stress due to COVID-19 and risk/protective variables in predicting psychopathology. Methods: Participants were 877 adults (73.7% female) recruited via internet sources from around the globe, but primarily residing in North America (87.4%). Results: Structural equation modelling revealed that certain risk and protective factors (e.g., loneliness, latent protective factor, mindfulness) were broadly related to psychopathology, whereas others showed unique relations with specific forms of psychopathology (e.g., greater repetitive thinking and anxiety; low meaning and purpose and depression). COVID-19 objective stress interacted with risk factors, but not protective factors, to predict greater anxiety symptoms, but not other forms of psychopathology. Limitations: This is a cross-sectional study of non-randomly recruited participants who reported high levels of income and education. Rates of problematic alcohol use were low. Conclusions: Findings contribute to our understanding of psychological mechanisms underlying individual differences in psychopathology in the context of a global stressor. Strategies that reduce loneliness and increase mindfulness will likely impact the greatest number of mental health symptoms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".