21. Optimal care in rheumatoid arthritis: Preliminary findings from a focus group study
Bibliographic record
Abstract
The objective of our work was to identify facilitators of optimal care, as well as potential barriers, for patients with rheumatoid arthritis (RA). The design was a focus group study. Individuals with established RA were identified through invitation letters sent using a random sample of the Quebec Arthritis Society mailing list. Patients were eligible for participation if they had a diagnosis of RA confirmed by a rheumatologist and if they had sought care within the McGill Réseau Universitaire Intégré de Santé network. We planned a series of focus group meetings (90 minutes each) to obtain sufficient data in terms of spectrum of ideas. In each moderator-led group, participants were asked to discuss five questions related to quality care. A co-moderator was available to document non-verbal communication, with audio-taping of all sessions and professional transcription for data analysis. Qualitative content analysis, based on grounded theory, was the chosen means of identifying recurring themes and categories. Two focus group sessions have been completed with two more scheduled. Preliminary findings indicate the importance of good communication between family physicians, specialists, and allied health care workers. Final coding of transcripts and computer-assisted content analysis is being completed. However it appears that focus group may be useful in studying optimal care for chronic diseases such as RA. Our preliminary findings emphasize the necessity of good communication among health care providers. Ultimately we hope to generate knowledge that can be transformed into better health for Canadians with arthritis and other chronic diseases.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".