An Initial Scoping Review of Dysregulation of Mood, Energy, and Social Rhythms Syndrome (DYMERS) Regarding Burnout in Healthcare Professionals During COVID-19
Bibliographic record
Abstract
Background/Objectives: Dysregulation of Mood, Energy, and Social Rhythms Syndrome (DYMERS) characterizes the poor regulation of biological (sleep/waking), social, and behavioral rhythms that affected the level of burnout in healthcare professionals during the pandemic in particular. The aim is to provide an initial scoping review of publications on this topic. Methods: The keywords “Stress Rhythms Dysregulation Bipolar Disorder Burnout DYMERS Healthcare professionals COVID-19” were searched on 9 December 2024 following PRISMA 2020 guidelines, using five primary databases (OVID, ProQuest, PubMed, Scopus, Web of Science), one register (Cochrane COVID-19 register), and one supplementary database (Google Scholar). Included were peer-reviewed publications. Excluded were duplicates, reports lacking either a research study or any keywords, or including irrelevant information regarding them. Results: The returns for all the databases were (n = 0) except for ProQuest (n = 4) and Google Scholar (n = 14). Of these, three ProQuest returns were duplicates of the Google Scholar search. The remaining report contained irrelevant information on healthcare professionals. The Google Scholar search results produced two relevant reports—neither duplicated with ProQuest. The excluded contained a duplicate in the search itself, three that did not mention healthcare professionals, two that contained irrelevant information concerning them, four returns that were not a research study, and three that were not peer-reviewed. Conclusions: The two studies published on this topic are by various members of the same investigating institution. DYMERS has provided valuable insights regarding burnout in healthcare professionals. The suggestion is for further DYMERS research by this team and others, anticipating future pandemics.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".