Collaborating with youth to inform and develop tools for psychotropic decision making.
Bibliographic record
Abstract
INTRODUCTION: Youth oriented and informed resources designed to support psychopharmacotherapeutic decision-making are essentially unavailable. This article outlines the approach taken to design such resources, the product that resulted from the approach taken, and the lessons learned from the process. METHODS: A project team with psychopharmacology expertise was assembled. The project team reviewed best practices regarding medication educational materials and related tools to support decisions. Collaboration with key stakeholders who were thought of as primary end-users and target groups occurred. A graphic designer and a plain language consultant were also retained. RESULTS: Through an iterative and collaborative process over approximately 6 months, Med Ed and Med Ed Passport were developed. Literature and input from key stakeholders, in particular youth, was instrumental to the development of the tools and materials within Med Ed. A training program utilizing a train-the-trainer model was developed to facilitate the implementation of Med Ed in Ontario, which is currently ongoing. CONCLUSION: An evidence-informed process that includes youth and key stakeholder engagement is required for developing tools to support in psychopharmacotherapeutic decision-making. The development process fostered an environment of reciprocity between the project team and key stakeholders.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".