Health uncertainty among healthcare workers during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: Health uncertainty among healthcare workers has yet to be examined as a contributor to the psychological toll of the COVID-19 pandemic. We aimed to (1) characterize health uncertainty levels among healthcare workers in a large, U.S. hospital system during the COVID-19 pandemic and (2) examine associations between health uncertainty and psychological outcomes.Methods: From March to June 2020, healthcare workers in a large, urban U.S. healthcare system were invited to complete an online questionnaire. Self-report measures assessed sociodemographic characteristics and job roles, health uncertainty, and emotional wellbeing variables (anxiety, depression, loneliness, self-compassion, and coping confidence). Health uncertainty (categorical and continuous scores) was compared across each variable using correlations and ANOVAs.Results: Healthcare workers (N = 440) were on average 44.5 years of age, 88.9% female, and 84.5% non-Hispanic white. Over half (52%) endorsed experiencing health uncertainty “sometimes” to “all the time”. While unrelated to sociodemographic characteristics (ps > .05), health uncertainty was highest among pharmacists and technicians, with levels significantly higher than other roles including physicians (p < .05) and mental health and spiritual counselors (p < .05). Higher health uncertainty was associated with higher anxiety (p < .001), depression (p < .001), and loneliness (p < .001), higher self-compassion (p = .02), and lower coping confidence (p < .001).Conclusions: Health uncertainty during the COVID-19 pandemic is common among healthcare workers, with higher levels related to poorer emotional wellbeing and less confidence in their coping abilities. Further research is needed to understand the relationships between healthcare workers’ health uncertainties and associated factors (i.e., job roles) and to identify whether health uncertainty may be a modifiable target for future interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".