Psychological distress is associated with symptoms of post-traumatic stress disorder among healthcare providers during the COVID-19 pandemic: 2021–2023
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, approximately 25% of healthcare providers (HCP) worldwide were reported to have experienced symptoms associated with post-traumatic stress disorder (PTSD). While longitudinal studies have identified factors associated with PTSD in this group of essential workers, associations with psychological distress trajectories have not been studied. Methods: Healthcare providers who participated in the prospective Canadian COVID-19 Cohort Study were eligible. Baseline data were collected at enrolment with time-varying measures updated by participants every 12 months. Kessler Psychological Distress Scale (K10) questionnaires were completed in March 2021 or upon their recruitment (whichever came first) and every 6 months thereafter. Impact of Event Scale-Revised (IES-R) questionnaires were completed within two weeks of their withdrawal from the study or study termination date (December 2023). Modified Poisson regression was used to assess the association between PTSD symptoms (i.e., IES-R scores of < 24 vs. ≥ 24) and score trajectories of the first four K10 questionnaires that were completed 180 (± 60) days apart. Results: = 111, 25.2%), chronically distressed (131, 29.7%), delayed onset of distress (43, 9.8%), recovery (83, 18.8%), and mutable (73, 16.6%). HCP whose K10 score trajectories were classified as chronically distressed (i.e., all ≥ 16) had rates of IES-R scores indicative of PTSD that were 6.9 times [95% confidence interval (CI) 3.7, 13.0] higher than HCP with resilient score trajectories (i.e., all < 16). Participants with scores in the other three K10 trajectories also had higher rates of IES-R scores of ≥ 24 when compared to those with resilient scores, with adjusted incident rate ratios of 2.6 (delayed onset; CI 1.3, 5.1), 3.1 (recovery; CI 1.4, 7.2), and 4.0 (mutable; CI 2.2, 7.3). Conclusion: Early and repeated assessment of HCP distress levels will help identify those who are distressed so that evidence-based mitigation strategies can be provided.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".