A Scoping Review of Burnout Avoidance by Employees During the COVID-19 Pandemic: The Role of Psychological Flow
Bibliographic record
Abstract
Background: Burnout represented a significant employee problem during the COVID-19 pandemic. Experiencing the psychological flow investigated by Csikszentmihalyi might avoid it. Yet, COVID-19 may have contributed to the unattainability of psychological flow for burnout-prone employees. The objective of this study is to determine the COVID-19 achievability of employee flow and, if attained, whether flow resulted in burnout avoidance during the pandemic. Method: This scoping review includes searches of six primary databases (CINAHL, OVID, ProQuest, PubMed, Scopus, Web of Science), two searches of one supplementary database (Google Scholar), and one register (Cochrane COVID-19 register) of the keywords “burnout, COVID-19, employees, healthcare providers, psychological flow, Csikszentmihalyi”. Included are peer-reviewed, COVID-19-related, 2020–2025 journal publications. Excluded are duplicates, non-COVID-19-related publications, reports lacking a research study, keywords, or relevant information. Results: In identifying 754 records, five records met the inclusion criteria. Mental healthcare practitioners, nurses, gig workers, corporate professionals, and working parents were the focus of the studies. Quantitative studies showed statistical significance. Qualitative studies showed promise for psychological flow mitigating burnout. Conclusions: Psychological flow was possible during COVID-19 for various employee types, and attaining it permitted burnout avoidance, suggesting a focus on achieving flow in the workplace during pandemics would diminish the incidence of employee burnout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".