Implementing a rapid-learning health system in early intervention services for psychosis: qualitative evaluation of its feasibility and acceptability
Bibliographic record
Abstract
BACKGROUND: Heterogeneity in implementing essential evidence-based early intervention for psychosis services (EIS) components persists despite existing fidelity standards/guidelines in many countries. Rapid-learning health systems (RLHS) may remedy these challenges, improving service delivery through systematic data collection, analysis, feedback and capacity-building activities. SARPEP (Système Apprenant Rapide pour les Programmes de Premiers Épisodes Psychotiques) is the first Canadian RLHS for EIS. This paper presents qualitative findings from the mixed-method study that evaluated the feasibility and acceptability of SARPEP. METHODS: We conducted six focus groups on the implementation of SARPEP with 25 participants from all SARPEP stakeholder groups; most were involved from project inception and throughout the 3-year implementation. The Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework (Glasgow, et al., 2019) and Lessard's dimensions for learning health systems guided data collection and deductive analysis. RESULTS: Reach: focus group participants reflected SARPEP reach and included all stakeholders involved (six service users, two family members, four psychiatrists, six managers, seven team leaders) who shared their experiences. EFFECTIVENESS: participants confirmed that SARPEP improved program capacity for data collection on core indicators and promoted evidence-based practices. Adoption: participants supported the selection of specific indicators and need to improve data-gathering technologies in the RLHS, even while challenges persisted regarding the integration of digital platform use by service users into routine care. Implementation and maintenance: all participants credited the RLHS with enabling mutual learning, self-reflection of programs and shared improvement of practices. CONCLUSIONS: SARPEP contributes to promote evidence-based care and a sense of belonging within the Quebec EIS network.
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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 | 0.071 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".