Perspectives of patients, first-degree relatives and rheumatologists on preventive treatments for rheumatoid arthritis: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that it may be possible to identify people at high risk of developing rheumatoid arthritis (RA). Assuming that effective interventions were available, this could mean that treatments introduced in the pre-symptomatic phase could prevent or delay the onset of the disease. Our study aimed to identify the potential attributes involved in decision-making around whether or not to take preventive treatment for RA, in order to inform the development of a discrete choice experiment (DCE) to ascertain consumer preferences for a preventive treatment program for RA. METHODS: We conducted a focus group study to develop conceptual attributes, refine their meaning, and develop levels. Participants included RA patients, first-degree relatives of RA patients, and rheumatologists who were 18 years of age and over, could read and speak English, and could provide informed consent. Candidate attributes were refined through iterative rounds of data collection and analysis. All focus groups were audio-recorded and transcribed, and then analyzed using the Framework Method to identify, compare, and contrast key conceptual attributes. RESULTS: Patients with RA, first-degree relatives of patients, and rheumatologists all valued the accuracy of testing due to concerns about false positives, and valued certainty in estimates of the test and preventive treatment. Patients and first-degree relatives desired this evidence from a range of sources, including discussions with people with the disease and health care professionals, and their preferences were modified by the strength of recommendation from their health care professional. CONCLUSIONS: are novel potential attributes involved in decisions around whether or not to take preventive treatment for RA, that have not been included in previous DCEs.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".