Examination of COVID stress syndrome facets and relations to substance misuse using profile analysis via multidimensional scaling (PAMS)
Bibliographic record
Abstract
The COVID-19 pandemic has contributed to significant societal challenges, including increased substance misuse. The COVID stress syndrome is a constellation of interrelated processes that occur in response to pandemics, including danger/contamination fears, fears concerning economic consequences, xenophobia, compulsive checking/reassurance-seeking, and pandemic-related traumatic stress symptoms. In the present study, using a sample of 812 adults collected during the early stages of the COVID-19 pandemic in May 2020, we examined the relations between identified profiles of the COVID Stress Scales (CSS) and behavioral and cognitive aspects of substance misuse. Using profile analysis via multidimensional scaling (PAMS), we identified two core profiles of the CSS, which explained 60% of the variance in participant responding: 1) High compulsive checking & Low xenophobia and 2) High xenophobia & Low danger/contamination. The first profile is consistent with the COVID stress syndrome, while the second profile aligns with the COVID disregard syndrome, which is a constellation of interrelated processes distinguished by a denial or downplaying of the seriousness of the COVID-19 pandemic and lack of perceived vulnerability to disease. Both profiles demonstrated significant positive correlations with drug and alcohol misuse, respectively. However, only the High xenophobia & Low danger/contamination profile demonstrated relations with cognitive aspects of substance misuse via positive and negative correlations with positive and negative expectancies of alcohol use, respectively. These findings provide further support for the relationship between the COVID stress syndrome and substance misuse and offer insight into how unique profiles of this syndrome may impact pandemic-related mental and public health interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".