Identifying Factors Influencing the Implementation of Early Intervention Services for Psychosis in Quebec, Canada: A Qualitative Study of Health Care Providers' Perspectives
Bibliographic record
Abstract
INTRODUCTION: Early intervention services for psychosis are clinically and cost-effective. Despite the availability of national and international guidelines and well-identified essential components of high-quality services, the implementation of early intervention services varies greatly. However, factors underpinning this are poorly understood and infrequently studied. In Quebec, the provision of earmarked funding and political support since 2017 has resulted in widespread dissemination of early intervention, which presented a valuable context to examine what shapes and influences the implementation of early intervention for psychosis. METHODS: An online survey was sent to leaders of all early intervention programmes (n = 33) in Quebec to assess service organisation and delivery. The survey's qualitative component included open-ended questions about factors impacting implementation, which are the focus of this report. Deductive and inductive thematic analysis was conducted through multiple iterations to reach consensus. RESULTS: Twenty-seven programmes responded to the questionnaire. Factors influencing implementation were separated into eight themes: human resources, workload, finances, physical resources, training, service delivery, service users and relationship with management. Every theme and subtheme was represented as a potential barrier or facilitator, with work atmosphere, quality of the clinic's premises, and management buy-in of early intervention more frequently noted as facilitators. CONCLUSION: Factors at the organisational, service and staff levels affect the implementation of early psychosis programmes. Despite political support and increased funding, insufficient funding and its consequences, along with limited implementation supports, remain important barriers to successful implementation. Rapid learning health systems can provide effective feedback to programmes to identify strategies to overcome identified barriers and enhance understanding of interactions between the identified factors. Lived experiences perspectives should also be included in future implementation research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".