Evaluation of Singapore’s Early Psychosis Intervention Programme (EPIP) using the First Episode Psychosis Services Fidelity Scale (FEP-FS)
Bibliographic record
Abstract
Background: Fidelity assessments are crucial for ensuring evidence-based delivery of first episode psychosis (FEP) services. The First Episode Psychosis Services Fidelity Scale (FEPS-FS) is a validated tool for evaluating FEP programs, but its application in Asian contexts remains unexplored. This study applies and adapts the FEPS-FS to evaluate Singapore’s Early Psychosis Intervention Programme (EPIP) in 2024, examining its contextual applicability within Singapore’s healthcare framework. Methods: Fidelity was assessed at EPIP through program data analysis, health record reviews, and interviews with staff, patients, and families. Three evaluators scored 37 items on a 5-point scale, with ratings determined by consensus. Two items were selectively added from an earlier FEPS-FS version to address local contexts. Items rated ≤3 indicated areas needing attention. The FRAME methodology documented adaptations for local challenges, particularly in weight management and substance use interventions. Results: EPIP demonstrated high fidelity to evidence-based FEP practices, particularly in program structure and clinical processes. Twenty items achieved maximum ratings, including team integration, family engagement, and clozapine adherence. Lower ratings (≤3) identified gaps in participant ratios, psychiatrist caseload, and medication practices. Five items, primarily related to psychological and occupational therapies and substance use interventions, were unscored due to documentation differences, highlighting the need for tailored adaptations. Conclusion: This study demonstrates EPIP’s strong adherence to evidence-based practices while highlighting areas for improvement, particularly in resource allocation and documentation. The findings underscore the need for culturally sensitive adaptations of fidelity measures and innovative solutions to address challenges in early psychosis services within Asian healthcare systems. Future research should focus on developing structured documentation systems that balance standardization with personalized care, and explore strategies to enhance metabolic health interventions and substance use management in the local context.
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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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".