Sleep Quality of Healthcare Professionals during the COVID-19 Pandemic in the Americas: a Qualitative Systematic Review and Meta-aggregation.
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic increased physical and mental stress and altered the work processes of healthcare professionals, impacting their sleep quality. The aim of the study was to identify and synthesize the perceptions and experiences of healthcare professionals, including nurses, doctors, and residents in the Americas, regarding sleep quality during the COVID-19 pandemic. Methods: The qualitative systematic review and meta-aggregation of qualitative data followed the JBI SUMARI protocol, based on PICO. Studies published between 2020 and 2023 were included. Data were extracted from databases: BIREME, PubMed, CINAHL, Embase, Scopus, Cochrane Library, Web of Science, Google Scholar, Cybertesis, and Canadian Dissertation and Theses. Qualitative data were separated and aggregated to domains. The quality of the studies was evaluated by JBI protocols, the level of evidence was analyzed. Results: Out of 900 screened and 47 selected studies, four analyzed 72 healthcare professionals, all nursing, with moderate study quality. Two main themes with high ConQual scores emerged: 1) the pandemic caused significant physical and mental health problems, which were either triggered by or expressed in sleep disorders; 2) professionals identified strategies to mitigate difficulties and challenges in the work environment. Conclusion: The pandemic had direct repercussions on the sleep quality of health professionals, highlighting the need for support programs and interventions to improve sleep quality.
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 | 0.055 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".