Patterns of Distress and Supportive Resource Use by Healthcare Workers During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: Healthcare workers (HCWs) are at increased risk of burnout, post-traumatic stress injury and suicide, compared to the public. Long-lasting increases in HCW distress are reported following pandemics. Such occupational stress can negatively impact individuals, organizations, and the overall healthcare system. Understanding HCW distress and needs can inform the development of resources to mitigate negative outcomes. Staff wellness data was gathered from a large academic health center during the COVID-19 pandemic, as part of a quality improvement project seeking to support staff wellbeing. Longitudinal trends of distress and preferences related to support were shared with leadership. Method: Monthly wellness assessments were sent to hospital staff via email. Assessments included screens for burnout, anxiety, depression and posttraumatic stress, questions regarding types of resources accessed, and open-ended questions regarding staff needs. Surveys were voluntary and confidential. Participants could provide their email to receive tailored resources based on individual results. Survey data was analyzed longitudinally to identify trends of distress over time. Results: A total of 2,518 wellness assessments were completed from April 2020-July 2021. An average of ~167 (range 17 – 946) HCWs responded per month and 638 staff provided their email addresses to receive a response; 497 of these completed assessments multiple times. The proportion of positive screens were, on average, 44%, 29%, 31% and 53%, for anxiety, depression, post-traumatic stress and burnout, respectively. Anxiety and post-traumatic stress scores decreased from April-August, then increased from September. The most reported source of support accessed was family/friends; ~40% of responders had not accessed formal mental health support. Conclusion: When COVID-19 cases decreased and stay-at-home mandates were lifted, HCW distress was reduced. Burnout trended upwards through the pandemic. Peer/family support remained favored compared to formal mental health support, suggesting the importance to HCW of social support. HCW reported a preference for convenient access to supportive resources.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".