The mental health impact of the COVID‐19 pandemic and exposure to other potentially traumatic events up to old age
Bibliographic record
Abstract
We investigated whether the impact of potentially traumatic events (PTEs) on trauma-related symptoms changes across the transitional adult lifespan (i.e., 16-100 years old) and if this association differs for self-reported COVID-19-related PTEs compared to other PTEs. A web-based cross-sectional study was conducted among 7,034 participants from 88 countries between late April and October 2020. Participants completed the Global Psychotrauma Screen (GPS), a self-report questionnaire assessing trauma-related symptoms. Data were analyzed using linear and logistic regression analyses and general linear models. We found that older age was associated with lower GPS total symptom scores, B = -0.02, p < .001; this association remained significant but was substantially weaker for self-reported COVID-19-related PTEs compared to other PTEs, B = 0.02, p = .009. The results suggest an association between older age and lower ratings of trauma-related symptoms on the GPS, indicating a blunted symptom presentation. This age-related trend was smaller for self-reported COVID-19-related PTEs compared to other PTEs, reflecting the relatively higher impact of the COVID-19 pandemic on older adults.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".