Patient Satisfaction with Pharmacist‐Led Collaborative Follow‐Up Care in an Ambulatory Rheumatology Clinic
Bibliographic record
Abstract
OBJECTIVES: Patient satisfaction is known to increase with pharmacist intervention in general outpatient clinics and with nurse-led care in rheumatology clinics. The aim of the present study was to describe and compare patient satisfaction with two different types of care: a pharmacist physician collaborative model and a traditional physician model in a rheumatology clinic setting. METHODS: A cross-sectional survey of inflammatory arthritis patients seen during a follow-up visit in Edmonton, Alberta, Canada, was conducted over a ten-week period. Patient satisfaction was measured using a modified version of the validated Leeds Satisfaction Questionnaire, which uses a five-point Likert scale to measure six dimensions of satisfaction, and compared between the collaborative care and traditional physician models. RESULTS: A total of 62 patients completed the questionnaire (21 collaborative care and 41 traditional physician model). The average age of respondents was 52 years and the majority were female. The mean score for satisfaction across the six dimensions was 4.56 in the collaborative care group and 4.30 in the traditional physician group (p = 0.02). Patient satisfaction in the collaborative care group was consistently higher across all dimensions. No difference was noted between participants seen for the first time compared with those seen two or more times by the pharmacist. CONCLUSIONS: A collaborative care model can exceed the already high expectations for care of patients with inflammatory arthritis. Our findings support the role of pharmacists using a collaborative care approach to care for patients in rheumatology clinics.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".