Compassion fatigue in healthcare providers during the COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has negatively impacted the psychological health and well-being of healthcare providers. An amplification in chronic stressors, workload and fatalities may have increased the risk of compassion fatigue and disrupted the quality of patient care. Although current studies have explored the general psychological status of healthcare providers during the COVID-19 pandemic, few have focused on compassion fatigue. The purpose of this review is to explore the impacts of the COVID-19 pandemic on compassion fatigue in healthcare providers and the repercussions of compassion fatigue on patient care. METHODS AND ANALYSIS: This scoping review will follow Joanna Briggs Institute and Arksey and O'Malley scoping review methodology. Comprehensive searches will be conducted in the following relevant databases: MEDLINE (Ovid), PsycINFO (Ovid), Embase (Ovid), CINAHL, Scopus, Web of Science. To expand the search, reference lists of included studies will be handsearched for additional relevant studies. Included studies must report on the impact of COVID-19 pandemic on compassion fatigue in healthcare providers and have been published in English since January 2020. ETHICS AND DISSEMINATION: This review does not require research ethics board approval. By examining the impacts of the COVID-19 pandemic on compassion fatigue in healthcare providers, this scoping review can offer important insight into the possible risks, protective factors and strategies to support healthcare providers' psychological health and patient care amidst persisting stressful conditions.
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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 | 0.074 | 0.067 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".