History of trauma and COVID-19-related psychological distress and PTSD
Bibliographic record
Abstract
The COVID-19 pandemic has a great impact on society as a whole. Yet the pandemic and associated mandatory lockdown in several countries may have increased the vulnerability of certain populations. The present study aimed to document the frequency of clinical level of psychological distress and COVID-19 related post-traumatic stress symptoms in youth during the first wave of the pandemic. The study more specifically explored the role of prior trauma and adverse life events as a vulnerability factor for negative outcomes. A sample of 4914 adolescents and young adults from the province of Quebec, Canada was recruited online through social networks during the first wave of COVID-19. Results revealed that 26.6% of youth displayed serious psychological distress and 20.3% probable PTSD symptoms. The number of past traumas and adversity experienced showed a dose-response relation with the prevalence of psychological distress and PTSD. After controlling for socio-demographic characteristics and COVID-19 related variables (exposure, fear, suspicion of having the infection), participants with a history of five traumas and more presented a two-fold risk of serious psychological distress and probable PTSD. Emotion dysregulation was also associated with an increased risk of symptoms while resilience was linked to a reduced risk of distress.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".