An evaluation of the performance of the Opioid Manager clinical tool in primary care: A qualitative study
Bibliographic record
Abstract
AIMS: The Opioid Manager (OM) is a point-of-care paper tool for physicians, which summarizes the Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain. To evaluate the efficacy of the OM, there is a need to better understand how physicians are using the OM, and how it is relevant to their practice. METHODS: Semistructured interviews were conducted with six family physicians in Ontario with clinical pain management experience. The interviews were analyzed using content analysis. The technique of "code-recode" was conducted by two analysts to verify content validity. RESULTS: The following main themes emerged: 1) OM as a communication tool; 2) OM as an educational tool; 3) OM as a clinical tool; 4) OM content/design; 5) OM benefits; 6) who the OM is used with; 7) OM potential; and 8) challenges of pain management. Physicians' commented the OM was a useful reference for helping their clinical decision making regarding opioids, and used it to educate and communicate with their patients/colleagues. Although many felt the content/design of the OM had a number of good features, there was a need for modifications (ie, merge with other tools and create electronic version). Given the challenges associated with pain management, a number of benefits were derived from using the OM (ie, protection and building therapeutic alliance), and respondents' felt the tool had the potential to meet a number of unmet needs related to opioid management. CONCLUSIONS: Overall, the OM was viewed positively for improving pain management practices but further work is required to refine the tool's potential.
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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 | 0.050 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".