Using a learning health system to integrate Peer Support in Early Intervention Services for psychosis in Quebec: Protocol for a Participatory, Mixed-Methods Study (the PAIRPEP project)
Bibliographic record
Abstract
Introduction: Since 2019, a rapid learning health system for Quebec’s Early Intervention Services for psychosis named SARPEP (Système Apprenant Rapide pour les programmes de Premiers Épisodes Psychotiques) operates to bridge the evidence-practice gap across the province. Despite strong stakeholder support and government recommendations, peer support services remained poorly available. To address this gap, since 2023, the PAIRPEP project was co-developed to support and evaluate the implementation of peer support and family peer support. This paper describes the co-designed study protocol, embedded within this learning health system. Methods: This participatory, mixed-methods study aims to examine the PAIRPEP intervention implementation longitudinally over 3 years across 12 Early Intervention Services and its impact on multiple stakeholders. Informed by the Medical Research Council framework for complex interventions, the project includes a co-designed (with multiple stakeholders) multimodal capacity-building program with specific components developed to overcome barriers to peer support and family peer support integration. Quantitative questionnaires are collected every four months from clinicians and continuously from youth and families (questionnaires, satisfaction surveys) using the learning health system electronic platform. Focus groups are conducted annually over three years with eight stakeholder groups. The analysis integrates findings using thematic synthesis and joint displays to assess convergence and divergence across methods and perspectives. Results and conclusion: This protocol paper outlines the study’s co-design, procedures, and anticipated contributions. Embedding large-scale innovative intervention implementation (such as peer support) within an RLHS can foster real-time feedback, iterative refinement, and inform clinical practice and policies.
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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 | 0.078 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".