First Episode Psychosis Services Fidelity Scale (FEPS-FS 1.0) and Manual
Bibliographic record
Abstract
The First Episode Psychosis Services Fidelity Scale and Manual support the reliable assessment of quality of care for early psychosis intervention teams that can be used for accreditation, quality improvement or research The First Episode Psychosis Services Fidelity Scale (FEPS-FS 1.0) is a highly reliable scale that assesses the degree to which mental health teams deliver specialized evidence-based care to people experiencing a first episode psychosis. The scale comprises 35 components each rated on a 1 to 5 scale. It has been used in the United States, Canada and Europe. It can be used for on site fidelity reviews, remote fidelity assessment or self-report. Published papers document its psychometric features and allow comparisons with a representative sample of US programs. It is suitable for research, quality improvement and accreditation. The Manual provides a practical guide for scoring a FEPS program against the criteria set out in the fidelity scale. It is designed to increase the reliability and consistency of ratings across different sites and assessors. It includes a definition and rationale for each component, data sources, decision rules and a structured interview guide. There are also modules to support training the key informant and data abstractor. Templates support structured feedback to programs for quality improvement. The scale can be adjusted to rate care for different diagnostic groups including the schizophrenia spectrum disorders, bipolar disorder and those with an attenuated psychosis syndrome.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".