What we know about fatigue self-management programs for people living with chronic conditions: A scoping review
Bibliographic record
Abstract
OBJECTIVE: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. METHODS: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. RESULTS: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. CONCLUSION: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. PRACTICE IMPLICATIONS: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".