How patients with gout become engaged in disease management: a constructivist grounded theory study
Bibliographic record
Abstract
BACKGROUND: Prior qualitative research on gout has focused primarily on barriers to disease management. Our objective was to use patients' perspectives to construct an explanatory framework to understand how patients become engaged in the management of their gout. METHODS: We recruited a sample of individuals with gout who were participating in a proof-of-concept study of an eHealth-supported collaborative care model for gout involving rheumatology, pharmacy, and dietetics. Semistructured interviews were used. We analyzed transcripts using principles of constructivist grounded theory involving initial coding, focused coding and categorizing, and theoretical coding. RESULTS: Twelve participants with gout (ten males, two females; mean age, 66.5 ± 13.3 years) were interviewed. The analysis resulted in the construction of three themes as well as a framework describing the dynamically linked themes on (1) processing the diagnosis and management of gout, (2) supporting management of gout, and (3) interfering with management of gout. In this framework, patients with gout transition between each theme in the process of becoming engaged in the management of their gout and may represent potential opportunities for healthcare intervention. CONCLUSIONS: Findings derived from this study show that becoming engaged in gout management is a dynamic process whereby patients with gout experience factors that interfere with gout management, process their disease and its management, and develop the practical and perceptual skills necessary to manage their gout. By understanding this process, healthcare providers can identify points to adapt care delivery and thereby improve health outcomes.
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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 | 0.046 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".