Fidelity, Implementation Determinants, and Patient-Level Outcomes Following Initial Implementation of NAVIGATE in the Early Psychosis Intervention—Spreading Evidence-Based Treatment (EPI-SET) Study
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: While early psychosis intervention (EPI) services are effective, care delivery is often inconsistent, particularly in recovery-oriented care. We hypothesized that facilitated implementation of NAVIGATE, an evidence-based, standardized model of coordinated specialty care, would increase fidelity to EPI standards and improve patient functioning in real-world settings. STUDY DESIGN: The Early Psychosis Intervention-Spreading Evidence-based Treatment (EPI-SET) study was a non-randomized effectiveness-implementation hybrid type III trial in 6 Ontario EPI programs. We used the First Episode Psychosis Services-Fidelity Scale-Revised (FEPS-FS-R) to measure fidelity to EPI standards at baseline (T0) and 12 months (T1). Scores ranged from 1 to 5, indicating poor (<3.5), fair (3.5-4), and good (≥4.0) adherence. The Heinrichs-Carpenter Quality of Life Scale (QLS) was used to determine change in functioning from baseline to 12 months (which roughly coincided with the T1 fidelity assessment). Implementation determinants were assessed using the Consolidated Framework of Implementation Research. STUDY RESULTS: FEPS-FS-R scores indicated good adherence for 19/29 items at T0 and 17/29 items at T1. Compared to T0, at T1, more psychosocial treatment items and fewer access and continuity items achieved fair or better adherence. Among the 100 participants who completed a baseline assessment, QLS total scores improved significantly (estimated change = 13.6, 95% CI: 9.6-17.7, P < .001) from T0 to T1. Implementation experiences varied across sites, with 4 of 6 organizations reporting overall positive experiences. CONCLUSIONS: Implementation of NAVIGATE was associated with improved fidelity to psychosocial components of care, with concomitant improvements in patient functioning. These findings can inform widespread implementation of NAVIGATE.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".