Assessment of Psychological Distress in Health-care Workers during and after the First Wave of COVID-19: A Canadian Longitudinal Study: Évaluation de la Détresse Psychologique Chez Les Travailleurs de la Santé Durant et Après la Première Vague de la COVID-19: une étude longitudinale canadienne
Bibliographic record
Abstract
OBJECTIVES: Health-care workers (HCW) exposed to COVID-19 are at risk of experiencing psychological distress. Although several cross-sectional studies have been carried out, a longitudinal perspective is needed to better understand the evolution of psychological distress indicators within this population. The objectives of this study were to assess the evolution of psychological distress and to identify psychological distress trajectories of Canadian HCW during and after the first wave of COVID-19. METHOD: fifth edition (PCL-5), the Generalized Anxiety Disorder-7, and the Patient Health Questionnaire-9. Descriptive statistics were used to illustrate the evolution of psychological distress indicators, whereas latent class analysis was carried out to identify trajectories. RESULTS: During and after the first wave of COVID-19, the rates of clinical mental health symptoms among our sample varied between 6.2% and 22.2% for post-traumatic stress, 10.1% and 29.9% for depression, and 7.3% and 26.9% for anxiety. Finally, 4 trajectories were identified: recovered (18.77%), resilient (65.95%), subchronic (7.24%), and delayed (8.04%). CONCLUSION: The longitudinal nature of our study and the scarcity of our data are unique among existing studies on psychological distress of HCW in COVID-19 context and allow us to contextualize prior transversal data on the topic. Although our data illustrated an optimistic picture in showing that the majority of HCW follow a resilience trajectory, it is still important to focus our attention on those who present psychological distress. Implementing preventive mental health interventions in our health-care institutions that may prevent chronic distress is imperative. Further studies need to be done to identify predictors that may help to characterize these trajectories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".