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Record W4391615464 · doi:10.5737/2368807634128

An Integrative Review of Effective Strategies to Prevent and Treat Compassion Fatigue in Oncology Nurses

2024· article· en· W4391615464 on OpenAlexaffvenue
Jodi Collier, Tania Bergen, Hua Li

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of SaskatchewanRed Deer PolytechnicAlberta Health Services
Fundersnot available
KeywordsCompassion fatigueBurnoutPsychological interventionMindfulnessMedicineNursingOncology nursingEmotional exhaustionNursing Interventions ClassificationOncologyClinical psychologyNurse education

Abstract

fetched live from OpenAlex

Compassion fatigue is understood as the combination of secondary traumatic stress and cumulative burnout caused by reduced ability to cope with one's environment. As such, compassion fatigue can be a significant workplace hazard for nurses in oncology. Findings from this integrative review reveal a lack of awareness and understanding of compassion fatigue among oncology nurses even if this group has been identified as high risk for experiencing compassion fatigue. Strategies such as self-care, mindfulness, and resiliency-based interventions to cope with compassion fatigue are reviewed herein along with related effectiveness. Some studies underscore that prevention-focused rather than treatment-focused interventions for compassion fatigue may be more effective. The responsibility for promoting and protecting oncology nurses' well-being is essential and must be spearheaded by organizations, administration, educational institutions, care teams, and individual nurses.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.058
GPT teacher head0.526
Teacher spread0.468 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2024
Admission routes2
Has abstractyes

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