Ohio Health Care Professionals’ Survey: Work and Home Stressors During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic brought unparalleled strain to the United States’ already overburdened health care workforce, and research is just beginning to shed light on its effects. This study sought to document health care pro-vider stressors during the pandemic to inform prevention and intervention strategies to better support their well-being. Methods: A one-time online survey was completed in July and August 2021 by Ohio health care professionals employed during the COVID-19 pandemic. We assessed for work and employment status changes and measured the severity of various work and home stressors among respondents who worked during the COVID-19 pandemic (N = 12 807). Results: Over a quarter of respondents had a change in work setting, and 59% had an increase in their workload; 20% of respondents were furloughed, laid off, or unemployed at some point during the COVID-19 pandemic. Over 37% reported a negative financial impact. The work stressors causing the greatest concern were spreading the virus and insuf-ficient communication from leadership. The primary home stressors were a lack of quality time with family and friends, being too tired when home from work to cook, do chores, etc, and being a supportive, present parent. At least half of the sample scored each of these as moderate, significant, or extreme stressors. Conclusion: The COVID-19 pandemic caused unrelenting stress affecting Ohio health care professionals at work and at home. Prevention and early intervention programs and public policies are required to prevent burnout and better support health care worker well-being.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".