Determination of Compassion Fatigue and Brain Fog Levels and Associated Factors Among Hemodialysis Unit Professionals: A Descriptive and Analytical Study
Bibliographic record
Abstract
INTRODUCTION: Hemodialysis professionals are particularly at risk due to the chronic nature of patient care and the intense emotional burden it entails. The aim of this study is to determine the levels of compassion fatigue and brain fog among healthcare professionals working in these units, to examine the relationship between them, and to identify their predictive factors. METHODS: This study employed a descriptive and analytical design, with data collected between February 16 and June 16, 2024. Reporting followed the STROBE checklist. The study population consisted of healthcare professionals working in private and public hemodialysis centers in Turkey. A non-probability snowball sampling method was used. Data were collected using a Descriptive Information Form, the Compassion Fatigue-Short Scale, and the Brain Fog Scale. RESULTS: Of the participants, 82.0% were female, and 33.6% were between 36 and 45 years of age. Participants reported moderate levels of compassion fatigue (59.67 ± 21.25) and high levels of brain fog (80.01 ± 26.53). A strong positive correlation was observed between compassion fatigue and brain fog (r = 0.744, p < 0.001). Compassion fatigue levels were significantly predicted by gender, profession, and brain fog (p < 0.005). Conversely, brain fog levels were significantly predicted by the institution of employment, profession, and compassion fatigue (p < 0.005). DISCUSSION: Healthcare professionals experienced moderate levels of compassion fatigue and high levels of brain fog, both of which can impair well-being and job performance. Early recognition and management of these conditions are crucial. Nursing practice and health policy should emphasize supportive interventions such as mental health programs, resilience training, and workload management to protect staff well-being and sustain quality patient care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".