Compassion fatigue in healthcare providers: a scoping review
Bibliographic record
Abstract
The detrimental impacts of COVID-19 on healthcare providers' psychological health and well-being continue to affect their professional roles and activities, leading to compassion fatigue. The purpose of this review was to identify and summarize published literature on compassion fatigue among healthcare providers and its impact on patient care. Six databases were searched: MEDLINE (Ovid), PsycINFO (Ovid), Embase (Ovid), CINAHL, Scopus, Web of Science, for studies on compassion fatigue in healthcare providers, published in English from the peak of the pandemic in 2020 to 2023. To expand the search, reference lists of included studies were hand searched to locate additional relevant studies. The studies primarily focused on nurses, physicians, and other allied health professionals. This scoping review was registered on Open Science Framework (OSF), using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review. From 11,715 search results, 24 met the inclusion criteria. Findings are presented using four themes: prevalence of compassion fatigue; antecedents of compassion fatigue; consequences of compassion fatigue; and interventions to address compassion fatigue. The potential antecedents of compassion fatigue are grouped under individual-, organization-, and systems-level factors. Our findings suggest that healthcare providers differ in risk for developing compassion fatigue in a country-dependent manner. Interventions such as increasing available personnel helped to minimize the occurrence of compassion fatigue. This scoping review offers important insight on the common causes and potential risks for compassion fatigue among healthcare providers and identifies potential strategies to support healthcare providers' psychological health and well-being.
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 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 | 0.029 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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; both teacher heads agree on what is shown here.
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".