Development and Testing of the First-Episode Psychosis Services Fidelity Scale
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to test the reliability and validity of the First-Episode Psychosis Services Fidelity Scale (FEPS-FS) and compare it with similar scales. METHODS: A fidelity scale was developed from previously identified essential components of first-episode psychosis services. The scale was tested in six programs in two countries and compared with three existing scales. RESULTS: Program data collection from multiple sources indicated the feasibility and reliability of the FEPS-FS (intraclass correlation coefficient for interrater reliability=.842; 95% confidence interval=.795-.882). Satisfactory programs scored an average of 86% of the maximum total score; the single unsatisfactory program scored 70%. Compared with the other scales, the FEPS-FS has fewer items, but it has the highest proportion of items common to all scales. CONCLUSIONS: The FEPS-FS is a feasible, compact, reliable, and valid measure of adherence to evidence-based practices for first-episode psychosis services that can be applied to any first-episode psychosis service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".