Bridging Science and Hope: integrating and Communicating Lived experience in Accelerating Medicines Partnership® Schizophrenia Program
Bibliographic record
Abstract
The Accelerating Medicines Partnership Schizophrenia (AMP® SCZ) program integrates lived experience into psychosis research, leveraging over three decades of foundational studies to improve research quality, promote community engagement, and ensure ethical implementation of precision psychiatry. Lived experience is embedded in the program’s governance, shaping study protocols, recruitment strategies, and digital tools such as the mindLAMP platform. Study sites also integrate lived experience through youth advisory boards, peer support specialists, and advisory committees, ensuring diverse perspectives inform research design and implementation. These efforts aim to develop predictive tools and therapeutic strategies while maintaining ethical and participant-centered practices. Advocacy organizations, such as the National Alliance on Mental Illness (NAMI), have fostered collaboration among government, industry, and academic partners, shaping outreach and engagement strategies. Dissemination efforts, led by the Website and Outreach Workgroup (WOW), include an accessible, Section 508-compliant website and co-designed resources, building trust and engagement within communities. By integrating lived experience at every stage, the program aims to foster trust, enhance research outcomes, and inform future strategies for treatment and prevention. Watch Dr. Tina Kapur, Dr. Kathryn Eve Lewandowski, and Dr. Carlos A. Larrauri discuss this article and their work at: https://vimeo.com/1050068801 .
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.032 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".