Evaluation of a pharmacist-led interprofessional chronic pain clinic in Canada
Bibliographic record
Abstract
Background: Chronic noncancer pain (CNCP) is a common condition that affects individuals at a biopsychosocial level and can significantly impair function and quality of life. Referral to an interprofessional CNCP program is recommended for most patients; however, these clinics are limited in number and capacity. Expanding access by testing new service delivery models would be of value. The purpose of this study was to measure the impact of a new pharmacist-led, interprofessional model of care developed at the University of Saskatchewan Chronic Pain Clinic. Methods: A retrospective chart audit was conducted using data that included adult patients referred for CNCP management between May 2020 and December 2021. Medication use, overall health status (using the Clinical Global Impression of Change-Improvement [CGI-I] scale) and patient readiness to change (using the Transtheoretical Model) were measured 6 months after the initial appointment. Results: The study included 138 patients. Of the 80 patients taking an opioid, 22.5% were switched to buprenorphine/naloxone and the remainder had their mean morphine-equivalent dose reduced by a mean of 41.7 mg/d. Overall patient health status was minimally improved and many patients moved into the Action stage of change. Discussion: Changes in opioid use demonstrate a clinically important shift toward safer medication regimens that are less likely to lead to toxicity and unintended overdose. CGI-I data suggest that these patients, whose health status is typically very difficult to change, did not deteriorate but slightly improved after attending the clinic. Conclusion: The unique pharmacist-led, interprofessional model of care used by the University of Saskatchewan Chronic Pain Clinic may offer a viable alternative to traditional physician-led models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".