<i>SCOPE of Pain</i> : An Evaluation of an Opioid Risk Evaluation and Mitigation Strategy Continuing Education Program
Bibliographic record
Abstract
OBJECTIVE: Due to the high prevalence of prescription opioid misuse, the US Food and Drug Administration (FDA) mandated a Risk Evaluation and Mitigation Strategy (REMS) requiring manufacturers of extended-release/long-acting (ER/LA) opioid analgesics to fund continuing education based on a FDA Blueprint. This article describes the Safe and Competent Opioid Prescribing Education (SCOPE of Pain) program, an ER/LA opioid analgesic REMS program, and its impact on clinician knowledge, confidence, attitudes, and self-reported clinical practice. METHOD: Participants of the 3-h SCOPE of Pain training completed pre-, immediate post- and 2-month post-assessments. SUBJECTS: The primary target group (n = 2,850), and a subset (n = 476) who completed a 2-month post-assessment, consisted of clinicians licensed to prescribe ER/LA opioid analgesics, who care for patients with chronic pain and who completed the 3-h training between February 28, 2013 and June 13, 2014. RESULTS: Immediately post-program, there was a significant increase in correct responses to knowledge questions (60% to 84%, P ≤ 0.02) and 87% of participants planned to make practice changes. At 2-months post-program, there continued to be a significant increase in correct responses to knowledge questions (60% to 69%, P ≤ 0.03) and 67% reported increased confidence in applying safe opioid prescribing care and 86% reported implementing practice changes. There was also an improvement in alignment of desired attitudes toward safe opioid prescribing. CONCLUSIONS: The SCOPE of Pain program improved knowledge, attitudes, confidence, and self-reported clinical practice in safe opioid prescribing. This national REMS program holds potential to improve the safe use of opioids for the treatment of chronic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".