215 Innovating Medical Education: A translational approach to integrating cannabinoid-based therapies for chronic pain management
Bibliographic record
Abstract
Objectives/Goals: This project aims to inform and develop a clinician-centered educational tool evidence-based and stakeholder-informed that fosters healthcare professionals’ (HCPs) adaptive expertise (AE) in cannabinoid-based therapies (CBT) for chronic pain management (CPM), addressing existing knowledge gaps, improving patient care and clinical decision-making. Methods/Study Population: To achieve this, the project will use a mixed-methods approach divided into three phases to evaluate existing educational resources, identify gaps, and inform the design of a curriculum to transform clinician education in CBT for CPM. It includes stakeholder mapping to engage and consult key experts for real-world insights and an environmental scan to assess and compare current educational resources qualitatively. A rigorous curriculum will be informed to be designed through an adaptive expertise and reflective practice framework, emphasizing case and problem-based learning, clinical simulations, and other pedagogical techniques. The educational tool will be pilot-tested with clinicians, measuring its impact on knowledge and decision-making flexibility through pre- and post-assessments, ensuring it fosters AE on CBT. Results/Anticipated Results: The project is expected to identify key gaps in existing educational resources, particularly AE in HCPs specializing in CPM. Through pilot testing, we anticipate improved knowledge of CBT among clinicians and enhanced ability to apply this knowledge flexibly in clinical practice. We also expect to establish core curriculum components that better support routine and adaptive expertise in chronic pain management. The pilot evaluations will guide further curriculum refinement and inform broader educational implementation. Discussion/Significance of Impact: This project addresses critical gaps in CbT education by informing the development of a curriculum that enhances clinicians’ ability to manage chronic pain with cannabinoid-based therapies. The resulting educational tool could significantly impact clinical practice, empowering patients, and HCPs to make informed decisions and improve patient outcomes.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".