Nurse-led Care for Patients with Rheumatoid Arthritis: A Systematic Review of the Effect on Quality of Care
Bibliographic record
Abstract
OBJECTIVE: In the nurse-led care (NLC) model, nurses take on the primary responsibility for patient management. We systematically assessed the effect of NLC for patients with rheumatoid arthritis (RA) on multiple dimensions of quality of care from the Alberta Quality of Care Matrix for Health. METHODS: We searched MEDLINE, EMBASE, and CINAHL from 1950 to January 2015. English-language studies were included if they reported on NLC for patients with RA and assessed 1 or more dimensions of quality (effectiveness, acceptability, efficiency, accessibility, appropriateness, and safety). Data were synthesized using narrative analysis. RESULTS: We included 10 studies. The NLC models varied in terms of nurses' professional designation (clinical nurse specialists or nurse practitioners); however, their role in the clinic was fairly consistent. Disease activity was the most common measure of effectiveness, with NLC being equal (n = 2) or superior (n = 3) to the comparator. NLC was equal (n = 1) or superior (n = 5) versus the comparator in terms of patient satisfaction (i.e., acceptability of care). NLC was equally safe as other models (n = 2). Regarding efficiency, results varied across studies (n = 6) and did not allow for conclusions about models' cost-effectiveness. In qualitative studies, patients found NLC to be superior in terms of accessibility [i.e., continuity of care (n = 3) and appropriateness measured with education and support (n = 4)]; however, no quantitative measures were found. CONCLUSION: NLC for patients with RA is effective, acceptable, and safe as compared with other models. However, current evidence is insufficient to draw conclusions about its efficiency, accessibility, and appropriateness.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".