Burnout and compassion fatigue among organ and tissue donation coordinators: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To collate and synthesise available literature on burnout and compassion fatigue (CF) among organ and tissue donation coordinators (OTDCs) and to respond to the research question: what is known about burnout and CF among OTDCs worldwide? DESIGN: Scoping review using Joanna Briggs Institute methodology for scoping reviews. DATA SOURCES: Medline, EMBASE, PsycINFO, CINAHL, LILACS, PTSpubs and grey literature (ResearchGate, OpenGrey, Organ Donation Organization (ODO) websites, open access theses and dissertations) up to April 2020. STUDY SELECTION: Studies reporting aspects of burnout and CF among OTDCs, including risk and protective factors. DATA EXTRACTION: Two reviewers independently screened the studies for eligibility and extracted data from chosen sources using a data extraction tool developed for this study; NVIVO was used to perform a qualitative directed content analysis. RESULTS: The searches yielded 741 potentially relevant records, of which 29 met the inclusion criteria. The majority of articles were from the USA (n=7, 24%), Canada (n=6, 21%) and Brazil (n=6, 21%), published between 2013 and 2020 (n=13, 45%) in transplant journals (n=11, 38%) and used a qualitative design approach (n=12, 41%). In the thematic analysis, we classified the articles into five categories: (1) burnout characteristics, (2) CF characteristics, (3) coping strategies, (4) protective factors and (5) ambivalence. CONCLUSION: We identified aspects of burnout and CF among OTDCs, including defining characteristics, demographic predispositions, protective factors, coping strategies, precursors, consequences and personal ambivalences. Researchers described burnout and CF characteristics but did not use consistent terms when referring to CF and burnout, which may have hindered the identification of all relevant sources. This gap should be addressed by the application of consistent terminology, systematic approaches and appropriate research methods that combine quantitative and qualitative investigation to examine the underlying reasons for the development of burnout and CF among OTDCs.
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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 | 0.030 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".