A Scoping Review of Burnout Avoidance by Employees During the COVID-19 Pandemic: The Role of Psychological Flow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it