The Mental Health Impact of the COVID-19 Pandemic Among Physicians, Nurses, and Other Health Care Providers in Alberta: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, threats to mental health, psychological safety, and well-being are evident, particularly among the first responders and the health care staff. OBJECTIVE: This study aims to examine the prevalence and potential predictors of the likely stress, generalized anxiety disorder, and major depressive disorder among health care workers (HCWs). METHODS: A cross-sectional survey was used through a survey link sent to gather demographic information and responses on several self-report scales, including the Perceived Stress Scale, the Generalized Anxiety Disorder 7-item scale, and the Patient Health Questionnaire-9 among HCWs enrolled in the Text4Hope program. RESULTS: The result from this study suggests that during the COVID-19 pandemic, HCWs reported a high likelihood of moderate-to-high perceived stress (n=840, 81.2%), moderate-to-severe anxiety (n=369, 38.6%), and depression (n=317, 32.7%) symptoms. Nurses and other HCWs were significantly more likely to report depressive symptoms compared to physicians (F(2, 159.47)=15.89, 95% CI -5.05 to -2.04). Younger age groups of HCWs (≤30 years) were more prone to report likely stress, anxiety, and depressive symptoms compared to HCWs 41-50 and >50 years old (odds ratio [OR] 1.82-3.03). Similarly, females and those who reported a lack of social support (separated/divorced and single) among HCWs had a higher likelihood to report likely stress and depressive symptoms, respectively (OR 1.8 and 1.6, respectively). CONCLUSIONS: This cross-sectional study explored a high level of mental health burdens during the COVID-19 pandemic among HCWs in Alberta. Levels of psychological symptoms were more noticeable in the female gender and the nursing profession.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".