Exploring the Experience of Healthcare Workers Who Returned to Work After Recovering From COVID-19: A Qualitative Study
Bibliographic record
Abstract
Background: To date, a large body of literature focuses on the experience of healthcare providers who cared for COVID-19 patients. Qualitative studies exploring the experience of healthcare workers in the workplace after recovering from COVID-19 are limited. This study aimed to describe the experience of healthcare workers who returned to work after recovering from COVID-19. Methods: This study employed a qualitative descriptive approach with a constructionist epistemology. Data were collected through semi-structured in-depth interviews with 20 nurses and physicians, and thematic analysis was used to identify themes from the interview transcripts. Results: Three major themes about the psychological experiences of healthcare workers who had recovered from COVID-19 and returned to work were identified: (1) holding multi-faceted attitudes toward the career (sub-themes: increased professional identity, changing relationships between nurses, patients, and physicians, and drawing new boundaries between work and family), (2) struggling at work (sub-themes: poor interpersonal relationships due to COVID-19 stigma, emotional symptom burden, physical symptom burden, and workplace accommodations), (3) striving to return to normality (sub-themes: deliberate detachment, different forms of social support in the workplace, and long-term care from organizations). Conclusions: The findings have highlighted opportunities and the necessity to promote health for this population. Programs centered around support, care, and stress management should be developed by policymakers and organizations. By doing this, healthcare workers would be better equipped to face ongoing crises as COVID-19 continues.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".