Matryoshka Project: lessons learned about early intervention in psychosis programme development
Bibliographic record
Abstract
AIM: This part of the Matryoshka project sought to understand the processes with which early intervention in psychosis (EIP) programmes were implemented and developed. The goals were to understand the key influences of programme implementation in the context of rapid EIP service growth and lack of specific provincial guidelines. METHODS: Sampling was purposive and data were collected with semi-structured interviews. Five Matryoshka Project programmes were successfully contacted. All interviews were conducted by phone, recorded and transcribed verbatim. Emerging themes were analysed iteratively and discussed among authors. Key themes were validated with participants. RESULTS: The new EIP services were significantly influenced by the provincial EIP network, advocacy groups and clinical mentors. EIP programme decision makers often relied on each other for guidance. Although the research evidence assisted programme decision makers to develop an effective EIP model for their region, implementation was often shaped by funding constraints. Programmes adapted their EIP models according to funding and local service characteristics. The lack of specific guidelines may have allowed innovation; programme creativity and diversity is consistent with EIP values. Despite the challenges related to geography and staffing, programmes experienced important successes such as partnerships across sectors, quality clinical service and the ability to engage hard-to-serve clientele. CONCLUSIONS: Although important, research evidence played only a secondary role. Relationships among providers and services, coupled with the dedication of front-line staff, were more critical to knowledge exchange than written documents alone. These findings stress the importance of researcher-front-line relationships to the adoption of evidence-informed practice.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".