The Impact of Post‐COVID‐19 Condition on Frontline Healthcare Workers: A Scoping Review
Bibliographic record
Abstract
The main goal of this integrative scoping review was to address the knowledge gap and inform policy and research regarding the impact of post-COVID-19 conditions on frontline healthcare workers (HCWs). An integrative scoping review using Arksey and O'Malley's framework examined post-COVID-19 conditions in frontline HCWs. We searched CINAHL, EMBASE, APA PsycINFO, PubMed, Social Science Database, ProQuest, Social Science Journals, and Web of Science, including dissertations, conference proceedings, and government publications for gray literature. A preestablished data extraction tool was developed to capture relevant information about post-COVID-19 conditions in HCWs. Of the total 42 studies, the majority were cross-sectional in design (29) and conducted mainly in countries such as Italy (4), India (3), and Brazil (3). Study findings reveal that a substantial proportion of HCWs in various countries were diagnosed with post-COVID-19 condition, which included persistent symptoms affecting physical and mental well-being. Persistent symptoms, particularly fatigue and anxiety, were associated with a poorer quality of life, decreased work ability, and impaired health-related quality of life among HCWs. Fatigue was a frequently reported symptom in many studies, often accompanied by weakness, muscle pain, shortness of breath, anxiety, depression, and sleep disturbances. The evidence generated through this research examining post-COVID-19 conditions among HCWs is a foundation for informing policy in the healthcare workforce. These findings also address the gap in research on the broader impacts of the COVID-19 pandemic on employers and the healthcare workforce.
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".