Non-pharmacological interventions for self-management of fatigue in adults: An umbrella review of potential interventions to support patients recovering from critical illness
Bibliographic record
Abstract
PURPOSE: Fatigue is a common symptom after critical illness. However, evidence-based interventions for fatigue after critical illness are lacking. We aimed to identify interventions to support self-management of fatigue caused by physical conditions and assess their effectiveness and suitability for adaptation for those with fatigue after critical illness. MATERIALS AND METHODS: We conducted an umbrella review of systematic reviews. Databases included CINAHL, PubMed, Medline, PsycINFO, British Nursing Index (BNI), Web of Science, Cochrane Database of Systematic Reviews (CDSR), JBI Evidence Synthesis Database, and PROSPERO register. Included reviews were appraised using the JBI Checklist for Systematic Reviews and Research Syntheses. Results were summarised narratively. RESULTS: Of the 672 abstracts identified, 10 met the inclusion criteria. Reviews focused on cancer (n = 8), post-viral fatigue (n = 1), and Systemic Lupus Erythematosus (SLE) (n = 1). Primary studies often did not address core elements of self-management. Positive outcomes were reported across all reviews, and interventions involving facilitator support appeared to be most effective. CONCLUSIONS: Self-management can be effective at reducing fatigue symptoms and improving quality of life for physical conditions and has clear potential for supporting people with fatigue after critical illness, but more conclusive data on effectiveness and clearer definitions of self-management are required.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".