Family Medicine Physicians' Views of How to Improve Chronic Pain Management
Bibliographic record
Abstract
PURPOSE: To determine family practice provider views of how to improve chronic nonmalignant pain (CNMP) management in primary care. METHODS: Modified Delphi group process with providers randomly selected from 6 community practice sites: 3 federally qualified community health centers, 1 rural health center, and 2 hospital-owned practices. Providers gave structured written feedback in response to a report of provider and patient concerns about the quality of CNMP in their practice sites and participated in a facilitated discussion in 1 of 3 group meetings. RESULTS: 54% participation (n=14) of family physicians, 6 to 30 years out of residency, identified 4 major themes for improvement of CNMP treatment: (1) the need for provider practice guidelines; (2) changes in the monthly opioid prescription refill process; (3) provision of self-management support and access to alternative treatments for patients; and (4) the use of a nurse care manager. CONCLUSIONS: Family physicians identified multiple components of practice that would improve both provider and patient experiences during and outcomes of CNMP management. Recommendations lend themselves to consideration of CNMP as a chronic illness and use of the Chronic Care Model as an appropriate framework for quality improvement.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".