Self-management interventions for chronic disease: a systematic scoping review
Bibliographic record
Abstract
OBJECTIVE: To investigate the contributions of physiotherapy and occupational therapy to self-management interventions and the theoretical models used to support these interventions in chronic disease. DATA SOURCES: We conducted two literature searches to identify studies that evaluated self-management interventions involving physiotherapists and occupational therapists in MEDLINE, the Cochrane Library, CINAHL, EMBASE, AMED (Allied and Complementary Medicine), SPORTdiscus, and REHABDATA databases. STUDY SELECTION: Four investigator pairs screened article title and abstract, then full text with inclusion criteria. Selected articles (n = 57) included adults who received a chronic disease self-management intervention, developed or delivered by a physiotherapist and/or an occupational therapist compared with a control group. DATA EXTRACTION: Four pairs of investigators performed independent reviews of each article and data extraction included: (a) participant characteristics, (b) the self-management intervention, (c) the comparison intervention, (d) outcome measures, construct measured and results. DATA SYNTHESIS: A total of 47 articles reported the involvement of physiotherapy in self-management compared with 10 occupational therapy articles. The type of chronic condition produced different yields: arthritis n = 21 articles; chronic obstructive pulmonary disease and chronic pain n = 9 articles each. The theoretical frameworks most frequently cited were social cognitive theory and self-efficacy theory. Physical activity was the predominant focus of the self-management interventions. Physiotherapy programmes included disease-specific education, fatigue, posture, and pain management, while occupational therapists concentrated on joint protection, fatigue, and stress management. CONCLUSIONS: Physiotherapists and occupational therapists make moderate contributions to self-management interventions. Most of these interventions are disease-specific and are most frequently based on the principles of behaviour change theories.
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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 | 0.028 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".