Assessment of Psychological Distress in Health Care Workers During the First two Waves of COVID-19: A Follow-up of a Canadian Longitudinal Study
Bibliographic record
Abstract
Background: Health care workers (HCW) exposed to COVID-19 risk experiencing psychological distress. Little is known regarding longitudinal perspectives and evolutions of psychological distress within this population. The objective of this study is to extend the results of our previous study to the pandemic's second wave. Method: This prospective cohort study was conducted from May 8, 2020, to January 24, 2021, and includes 787 HCW. Symptoms of anxiety and depression were assessed using the Generalised Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Descriptive statistics illustrated the evolution of psychological distress indicators, whereas latent class analysis helped identify trajectories. Results: The results showed that a lower proportion of HCW exceeded the clinical threshold during the second wave (36,5% vs. 31,1%). As in the first wave, most of our sample fell onto the resilient trajectory (67.22%). We adapted the name of the remaining trajectories to better suit their evolution: rapid recovery (15.76%), slow recovery (9.66%), and delayed (7.37%). Conclusion: Approximately two-thirds of the HCW did not manifest significant distress. For those who did, the distress was transient. We observed a trend of positive adaptability among HCW, considering that the proportion of HCW experiencing psychological distress exceeding clinical threshold remained lower than during the first wave. Our data highlight the dynamic nature of psychological distress. To be able to detect psychological distress as it arises, HCW should use self-monitoring as an essential tool. This vigilance would allow institutions to offer timely support and resources for those experiencing psychological distress.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".