Renegotiating environments to achieve participation: A metasynthesis of qualitative chronic disease research: Restructurer les environnements pour favoriser la participation / Une métasynthèse de la recherche qualitative sur les maladies chroniques
Bibliographic record
Abstract
BACKGROUND: Qualitative studies describe environmental influences on participation in adults with chronic disease, but translating these findings into practice can be difficult. PURPOSE: This study sought to synthesize qualitative research findings regarding the influence of environmental factors on participation among adults with chronic disease. METHODS: Searching revealed 31 I articles that describe the link between environment and participation for adults with osteoarthritis, rheumatoid arthritis, diabetes mellitus, heart disease, cancer, chronic obstructive pulmonary disease, and/or depression. Study findings were analyzed using metasynthesis methods to identify themes. FINDINGS: For adults with chronic disease, renegotiating their environments and occupations to achieve, maintain, or rework their participation involves understanding support processes, being ordinary and able, navigating systems, and navigating physical environments. IMPLICATIONS: Key areas that occupational therapy interventions can target are facilitating constructive collaboration between client and support person, fostering connections with others, recognizing cultural pressure to be ordinary and able, and advocating for supportive policy and practice.
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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 | 0.173 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".