Treatment Preferences of Patients with Chronic Obstructive Pulmonary Disease: Results from Qualitative Interviews and Focus Groups in the United Kingdom, United States, and Germany
Bibliographic record
Abstract
BACKGROUND: A wide range of therapeutic regimens, including single-inhaler triple therapies (SITTs), are now available for the maintenance treatment of chronic obstructive pulmonary disease (COPD). Thus, an improved understanding of patient preferences may be valuable to inform physician prescribing decisions. This study was performed to assess the factors considered by patients when making decisions about their COPD treatments using qualitative techniques. METHODS: In the United Kingdom, United States and Germany, individual qualitative interviews (n=10 per country) and focus groups (1 per country; [United Kingdom, n=4; United States, n=6; Germany, n=6 participants]) were conducted. Interviews and focus groups were semi‑structured, lasting approximately 60 minutes, and focused on treatment preferences. Data were analyzed according to emerging themes identified from the interviews; qualitative thematic analysis of the data was performed using specialist software. RESULTS: In interviews and focus groups, efficacy, ease of use, and lower frequency of use were favored attributes for current treatment, while side effects, medication taste, and more complex administration techniques were key dislikes. In interviews, most participants would consider a switch in medication, mainly for improved efficacy, but also to reduce medication frequency or following physician advice. Overall, efficacy and ease of use were the 2 most important attributes reported in interviews in all 3 countries. CONCLUSION: Patients with COPD have preferences for certain attributes of medication, highlighting the multi-faceted nature of treatment effectiveness and the importance of the delivery device.These results were subsequently used to inform the design of a discrete choice experiment.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".