Rheumatologists’ Views and Perceived Barriers to Using Patient Decision Aids in Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: To explore rheumatologists' perceptions of patient decision aids (PtDAs) and identify barriers to using them in clinical practice. METHODS: A cross-sectional online survey of all members of the Canadian Rheumatology Association (CRA; n = 459) was conducted. We subsequently invited 10 respondents to participate in a 30-minute telephone interview to further explore their views on using PtDAs in clinical practice. Interview participants were purposefully sampled to achieve a balance in sex, years in clinical practice, and types of practice. RESULTS: In August and September 2013, 153 CRA members responded to the survey (response rate 33.3%); of those, 113 completed the entire questionnaire. Sixty-three respondents (55.8%) were male, 54 (47.8%) were ≥50 years of age, and 55 (48.7%) practiced in a multidisciplinary setting. When asked about their intention to use PtDAs, participants rated mean ± SD 5.7 ± 2.9 (where 0 = not likely and 10 = very likely). Sixty-four (56.6%) believed that rheumatologists were unfamiliar with PtDAs, and 76 (67.3%) thought that PtDAs would disturb their workflow. In-depth interviews revealed the following: the perception that PtDAs were no different from any other patient education tools, the concern that PtDAs were of limited value in real life since they relied solely on data from randomized controlled trials, and the fear that PtDAs could impair doctor-patient communication. CONCLUSION: There was a sense of ambivalence among rheumatologists about PtDAs. Our interviews further revealed concerns regarding the utility and benefits of PtDAs in clinical practice. The results show a need to familiarize physicians with PtDAs and to develop strategies to support their integration in clinical 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 | 0.024 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".