Compassion fatigue in surgical oncologists: A scoping review.
Bibliographic record
Abstract
343 Background: The practice of clinical oncology includes diverse and complex clinical, interpersonal and ethical challenges that can lead to physical and/or emotional distress, including burnout (BO), compassion fatigue (CF), secondary traumatic stress (STS) or moral distress (MDS), which potentially impacts patient care and provider well-being. Surgical oncology presents additional stressors and challenges, yet little is known about CF, STS, and MDS in this population, and the greater body of literature in oncology is heterogenous.The aim of this paper is to review current literature regarding CF, MDS, and STS in clinical oncologists, with a focus on surgical oncologists. Methods: Searches of OVID Medline and Embase databases were performed, as well as relevant bibliographies to identify articles related to CF, STS and MDS in clinical and surgical oncologists. Descriptive analysis was completed on relevant articles to address common definitions, themes, and potential aggravating/protective factors. Results: 619 articles were retrieved, of which 196 underwent data extraction. Of these, 48 articles were related to CF, MDS, or STS in oncologists and 5 included surgical oncologists. There was no data specific to surgical oncologists. Definitions of the terms were inconsistent across the literature. Potential contributing factors for CF/STS/MDS are related to the work environment, time pressures, and poor communication skills. In contrast, self-care, supportive colleagues/supervisors, and experience appear to be protective. Conclusions: This study highlights a need for standardized definitions to accurately capture and explore each of these phenomena. Further research is needed to provide insight into the challenges faced by surgical oncologists and how to support them.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".