The good, the bad, and the mixed: Experiences during COVID-19 among an online sample of adults
Bibliographic record
Abstract
Studies have outlined the negative consequences of the COVID-19 pandemic to psychological health. However, the potential within-individual diversity of experiences during COVID-19, and how such experiences relate to indices of psychological distress and COVID-19-specific stressors, remains to be explored. A large online sample of American MTurk Workers (N = 3,731; Mage = 39.54 years, SD = 13.12; 51.70% female) completed short assessments of psychological distress, COVID-19-specific stressors (e.g., wage loss, death), and seven items assessing negative and positive COVID-19 experiences. Latent profile analyses were used to identify underlying profiles of COVID-19 experiences. A four-profile solution was retained representing profiles that were: (1) predominantly positive (n = 839; 22.49%), (2) predominantly negative (n = 849; 22.76%), (3) moderately mixed (n = 1,748; 46.85%), and (4) high mixed (n = 295; 7.91%). The predominantly positive profile was associated with lower psychological distress, whereas both the predominantly negative and high mixed profiles were associated with higher psychological distress. Interestingly, specific COVID-19 stressful events were associated with the high mixed profile. The present study challenges the narrative that the impacts of COVID-19 have been unilaterally negative. Future directions for research are proposed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".