Où en sommes-nous? An Overview of Successes and Challenges after 30 Years of Early Intervention Services for Psychosis in Quebec: Où en sommes-nous? Un aperçu des réussites et des problèmes après 30 ans de services d’intervention précoce pour la psychose au Québec
Bibliographic record
Abstract
INTRODUCTION: Over the last 30 years, early intervention services (EIS) for first-episode psychosis (FEP) were gradually implemented in the province of Quebec. Such implementation occurred without provincial standards/guidelines and policy commitment to EIS until 2017. Although the literature highlights essential elements for EIS, studies conducted elsewhere reveal that important EIS components are often missing. No thorough review of Quebec EIS practices has ever been conducted, a gap we sought to address. METHODS: Adopting a cross-sectional descriptive study design, an online survey was distributed to 18 EIS that existed in Quebec in 2016 to collect data on clinical, administrative, training, and research variables. Survey responses were compared with existing EIS service delivery recommendations. RESULTS: Half of Quebec's population had access to EIS, with some regions having no programs. Most programs adhered to essential components of EIS. However, divergence from expert recommendations occurred with respect to variables such as open referral processes and patient-clinician ratio. Nonurban EIS encountered additional challenges related to their geography and lower population densities, which impacted their team size/composition and intensity of follow-up. CONCLUSIONS: Most Quebec EIS offer adequate services but lack resources and organizational support to adhere to some core components. Recently, the provincial government has created EIS guidelines, invested in the development of new programs and offered implementation support from the National Centre of Excellence in Mental Health. These changes, along with continued mentoring and networking of clinicians and researchers, can help all Quebec EIS to attain and maintain recommended quality standards.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".