Eliciting Mental Models of Opioid Management: Comparison of Concept Mapping and Knowledge Audit Methods
Bibliographic record
Abstract
Context: The Collaborative Mentorship Network for Chronic Pain and Addiction (CMN) in Alberta has observed differences in understanding of opioid therapy between primary care physicians and pharmacists. We suspected that indicated that pharmacists and family physicians are approaching prescribing and managing opioid therapy with different mental models. Objective: To understand the systematic differences that exist between pharmacists’ and family physicians’ mental models of managing opioid therapy, as a guide to building common ground. Study Design and Analysis: Cross-sectional descriptive comparisons between groups. Framework-guided qualitative analysis. Setting: Family medicine and community pharmacy practices in Alberta, Canada. Population Studied: 6 family physicians and 6 pharmacists who have some experience managing care for those using opioid therapy, recruited purposively for variation in age, years in practice, and geographic location across Alberta. Intervention/Instrument: Cognitive Task Analysis – Knowledge Audit (all participants, individually) and Concept Mapping (4 physicians, 4 pharmacists, in two mixed groups) methods. Outcome Measures: Detailed description of mental models of opioid therapy. Recommendations based on findings. Results: Mental models among pharmacists differed according to the context in which they work (chronic pain clinic, community pharmacy working closely with opioid dependency clinic, independent pharmacy, chain pharmacy). Mental models among family physicians differed based on their past experiences working with patients and pharmacists, as well as by when they were trained and the guidelines learned at that time. Mental model differences between family physicians and pharmacists were primarily in the decision making, continuity, and management of irregularity realms. The two concept maps differed greatly in richness and consistency, the more involved group’s map being richer and more consistent. Recommendations to the CMN program included different content and structure approaches based on target audience mental models. Conclusions: Concept Mapping and Knowledge Audit methods of CTA deliver somewhat different and complementary information about mental models. Understanding how different team members’ mental models differ is key to improving team functioning and care coordination.
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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 | 0.094 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".