‘All illness is personal to that individual’: a qualitative study of patients’ perspectives on treatment adherence in bronchiectasis
Bibliographic record
Abstract
BACKGROUND: Adherence to treatment is low in bronchiectasis and is associated with poorer health outcomes. Factors affecting adherence decisions have not been explored in patients with bronchiectasis. OBJECTIVE: We aimed to explore patients' perspectives on adherence, factors affecting adherence decision making and to develop a conceptual model explaining this decision-making process in adults with bronchiectasis. METHODS: Adults with bronchiectasis participated in one-to-one semi-structured interviews. Interviews were audio-recorded, transcribed verbatim and analysed independently by two researchers using thematic analysis. Data from core themes were extracted, categorized into factors affecting adherence decision making and used to develop the conceptual model. RESULTS: Participants' beliefs about treatment, the practical aspects of managing treatment, their trust in health-care professionals and acceptance of disease and treatment were important aspects of treatment adherence. The conceptual model demonstrated that adherence decisions were influenced by participants' individual balance of barriers and motivating factors (treatment-related, disease-related, health-care-related, personal and social factors). CONCLUSION: Adherence decision-making in bronchiectasis is complex, but there is the potential to enhance adherence by understanding patients' specific barriers and motivators to adherence and using this to tailor adherence strategies to individual patients and treatments.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".