A community engaged learning model to expand student compassion and understanding of the complexities associated with substance use
Bibliographic record
Abstract
Stigma surrounding substance use disorders (SUDs) is widespread and has even been identified among healthcare professionals and trainees, highlighting the need for educational interventions that foster compassionate care, understanding, and emphasize approaches that reflect the complexities of drug use. Community engaged learning (CEL) is an evidence-based pedagogy designed to connect classroom learning with real-world applications. Through community driven collaborative projects that integrate classroom learning with real-world experience, students develop the ability to connect scientific knowledge with social understanding. These experiences, combined with structured reflection and traditional assessments, prepare the next generation of pharmacologists that find careers in healthcare, academia, drug discovery or public health to approach their work with greater compassion, and a nuanced awareness of the complexities faced by individuals and communities affected by substance use. In this commentary, we aim to advance the dialogue on how CEL can contribute to a more empathetic, evidence-based approach to SUD care, research, and policy for the next generation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".