English Language Proficiency, Health Literacy, and Trust in Physician Are Associated with Shared Decision Making in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Treat-to-target guidelines promote shared decision making (SDM) in rheumatoid arthritis (RA). Also, because of high cost and potential toxicity of therapies, SDM is central to patient safety. Our objective was to examine patterns of perceived communication around decision making in 2 cohorts of adults with RA. METHODS: Data were derived from patients enrolled in 1 of 2 longitudinal, observational cohorts [University of California, San Francisco (UCSF) RA Cohort and RA Panel Cohort]. Subjects completed a telephone interview in their preferred language that included a measure of patient-provider communication, including items about decision making. Measures of trust in physician, education, and language proficiency were also asked. Logistic regression was performed to identify correlates of suboptimal SDM communication. Analyses were performed on each sample separately. RESULTS: Of 509 patients across 2 cohorts, 30% and 32% reported suboptimal SDM communication. Low trust in physician was independently associated with suboptimal SDM communication in both cohorts. Older age and limited English proficiency were independently associated with suboptimal SDM in the UCSF RA Cohort, as was limited health literacy in the RA Panel Cohort. CONCLUSION: This study of over 500 adults with RA from 2 demographically distinct cohorts found that nearly one-third of subjects report suboptimal SDM communication with their clinicians, regardless of cohort. Lower trust in physician was independently associated with suboptimal SDM communication in both cohorts, as was limited English language proficiency and older age in the UCSF RA Cohort and limited health literacy in the RA Panel Cohort. These findings underscore the need to examine the influence of SDM on health outcomes in RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".