Nursing management of fatigue in cancer patients and suggestions for clinical practice: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Fatigue is a complex and frequent symptom in cancer patients, influencing their quality of life, but it is still underestimated and undertreated in clinical practice. The aims of this study were to detect the presence of fatigue in cancer patients, describe how patients and nurses perceived it and how nurses managed fatigue. METHODS: This is a mixed methods study. Data were collected in two oncological wards using the Brief Fatigue Inventory (BFI), an ad hoc questionnaire, patient interviews, focus groups with nurses and the review of nursing records. Interviews and focus groups were analysed through thematic analysis. We used SPSS 22.0 for quantitative data and Nvivo 10 for qualitative data analysis. RESULTS: A total of 71 questionnaires were analysed (39 males, mean age 65.7 years). Fatigue was reported 5 times (7%) in nursing records, while in 17 cases (23.9%) problems associated to it were reported. Twelve patients were interviewed. Five themes were identified: feeling powerless and aggressive, my strategies or what helps me, feeling reassured by the presence of family members, feeling reassured by nurses' gestures, and being informed. Three themes were identified through the focus groups: objectivity and subjectivity in the assessment of fatigue, nurses' contribution to the multidisciplinary management of fatigue, and difficulty in evaluating outcomes. CONCLUSIONS: The approach to the management of fatigue was unstructured. Patients were satisfied with the care they received but needed more information and specific interventions. Useful aspects were identified that could be used to change health professionals' approach towards the management of fatigue.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".