24 / DETERMINING THE ATTITUDES OF COPING WITH DISTRESS LEVELS OF ONCOLOGY PATIENT RELATIVES
Bibliographic record
Abstract
E. Akgu00fcn u00c7u0131tak1, S. Kav1.u20281Baskent University, Health Sciences Faculty Nursing Department, Ankara, Turkey.Introduction:Oncology nurses are regularly exposed to high-stress situations that may lead to compassion fatigue. Types of interventions and evidence of its effectiveness on compassion fatigue hasn't been established.ObjectivesThe aim of the review is to determine effectiveness of the interventions to manage compassion fatigue in oncology nurses.MethodsElectronic databases (CINAHLu00ae, PubMed and WOS) were used for searching the studies with the following key words u201ccompassion fatigueu201d, u201cinterventions or strategiesu201d and u201concology nursingu201d. The methodology used for this systematic review was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Identified publications were screened by using the following inclusion criteria; randomized controlled trials (RCTs), intervention studies and English language full text journals published in last 10 years. Descriptive studies, reviews, conference abstracts, letters to the editor were excluded.ResultsThe initial search identified 64 articles and 8 studies met eligibility criteria. Majority (5) of the studies from USA and rest of them were from Canada and Portugal. Non-randomized, pre-test and post-test design were used in intervention studies. Most of the studies were pilot and included small sample ranging from 10 to 45 participants.Effectiveness of the interventions were measured by The Professional Quality of Life Scale. Mindfulness, support group, knitting and education were applied over 4 to 6 weeks period. The interventions were effective in decreasing compassion fatigue level except one study that using mobile application.ConclusionsThe findings of these small scale studies support that interventions can be effective in reducing nurses compassion fatigue.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".