Psychological impact of the COVID-19 pandemic on hospital workers over time: Relationship to occupational role, living with children and elders, and modifiable factors
Bibliographic record
Abstract
The COVID-19 pandemic is expected to have a sustained psychological impact on healthcare workers. We assessed individual characteristics related to changes in emotional exhaustion and psychological distress over time. A survey of diverse hospital staff measured emotional exhaustion (Maslach Burnout Inventory) and psychological distress (K6) in Fall 2020 (T1) and Winter 2021 (T2). Relationships between occupational, personal, and psychological variables were assessed using repeated measures ANOVA. Of 539 T1 participants, 484 (89.9%) completed T2. Emotional exhaustion differed by occupational role (F = 7.3, p < .001; greatest in nurses), with increases over time in those with children (F = 8.5, p = .004) or elders (F = 4.0, p = .047). Psychological distress was inversely related to pandemic self-efficacy (F = 110.0, p < .001), with increases over time in those with children (F = 7.0, p = .008). Severe emotional exhaustion occurred in 41.1% (95%CI 36.6–45.4) at T1 and 49.8% (95%CI 45.4–54.2) at T2 (McNemar test p < .001). Psychological distress occurred in 9.7% (95%CI 7.1–12.2) at T1 and 11.6% (95%CI 8.8–14.4) at T2 (McNemar test p = .33). Healthcare workers' psychological burden is high and rising as the pandemic persists. Ongoing support is warranted, especially for nurses and those with children and elders at home. Modifiable protective factors, restorative sleep and self-efficacy, merit special attention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".