Reliability and Feasibility of the First-Episode Psychosis Services Fidelity Scale–Revised for Remote Assessment
Bibliographic record
Abstract
OBJECTIVE: The authors sought to evaluate the interrater reliability and feasibility of the First-Episode Psychosis Services Fidelity Scale-Revised (FEPS-FS-R) for remote assessment of first-episode psychosis programs according to the coordinated specialty care model. METHODS: The authors used the FEPS-FS-R to assess the fidelity of 36 first-episode psychosis program sites in the United States with information from three sources: administrative data, health record review, and phone interviews with staff. Four raters independently conducted fidelity assessments of five program sites by listening to each of the staff interviews and independently rating the two other data sources from each site. To calculate interrater reliability, the authors used intraclass correlation coefficients (ICCs) for each of the five sites and across the total scores for each site. RESULTS: Total interrater reliability was in the good to excellent range, with a mean ICC of 0.91 (95% confidence interval = 0.72-0.99, p<0.001). Two first-episode psychosis program sites (6%) achieved excellent fidelity, 25 (69%) good fidelity, and nine (25%) fair fidelity. Of the 32 distinct items on the FEPS-FS-R, 23 (72%) were used with good or excellent fidelity. Most sites achieved high fidelity on most items, but five items received ratings indicating low-fidelity use at most sites. The fidelity assessment proved feasible, and sites required on average 10.5 hours for preparing and conducting the fidelity review. CONCLUSIONS: The FEPS-FS-R has high interrater reliability and can differentiate high-, moderate-, and low-fidelity sites. Most sites had good overall fidelity, but the FEPS-FS-R identified some services that were challenging to implement at many sites.
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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 | 0.042 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".