Stress and Burnout Among Mental Health Staff During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has deeply altered social and working environments among health care workers. These health care workers are therefore at risk of additional psychosocial strain and ensuing metal health symptoms, which indirectly affects patient care. In this study, we aimed to assess the psychosocial and psychopathological impact of COVID-19 among acute care mental health and addictions staff. METHODS: This study is a cross-sectional survey and contains a sample size of 60 mental health and addiction acute care workers recruited from within Nova Scotia Health Authority. The survey was constructed using the online survey system, Opinio, and consisted of three sections: demographic variables (gender, age group and profession); the DASS-21 Questionnaire (which provides dimensional measures of stress, anxiety and depression); and the MBI-HSS (MP) Questionnaire (which measures three dimensions of burnout-emotional exhaustion, depersonalization and personal achievement). RESULTS: The majority of participants had at least one pathologic score on the DASS-21 and MBI-HSS (MP) sections (75.5% and 93.5%, respectively). The median severity on the DASS-21 and MBI-HSS (MP) were both moderate, with the younger age group (20-35 years) having more significant burnout scores (p = 0.0494). Simple logistic regression showed a significant relationship between burnout severity and pathologic distress, and simple linear regression showed significant correlation between DASS-21 and MBI-HSS (MP) scores, with a R2 value of 0.4633. CONCLUSION: More planning, programs, resources and further research are needed to support wellness and recovery of all health care professionals who work at the mental health and addictions acute care unit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".