A comparison of the National Clinical Audit of Psychosis 2019/2020 standards and First Episode Psychosis Services Fidelity Scale 1.0
Bibliographic record
Abstract
AIM: The authors compare two approaches to assessment of the quality of early psychosis intervention services, the National Clinical Audit of Psychosis used in the United Kingdom and the First Episode Psychosis Services Fidelity Scale used in North America and Europe. METHODS: We compare the two approaches on the source of standards, measurement type, data collection, time requirements, scoring and reliability. Finally, we review their strengths and limitations. RESULTS: Both measures are based on standards derived from the same research evidence base. Both methods rely on data from health records and administrative data. The audit is supplemented with user survey data, the fidelity scale with clinician interviews. The audit requires more time. The audit is based on quality indicators rated as present or absent which yields a statistical benchmark. The Fidelity Scale is based on quality indicators that are rated on a five-point scale yielding a standards-based measure. The two methods cover similar service components, but the FEPS-FS has a broader coverage of team functioning. The National audit also collects data on the user experience directly from patients. The fidelity scale has achieved good to excellent inter-rater reliability, the reliability of the audit has not been tested. CONCLUSIONS: Both methods have face validity and provide reliable and useful measures of quality of care. The NCAP works in the context of a single provider health system, the FEPS-FS works in a more variable health system. Comparing the two systems in the field would support international comparison of standards of care.
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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 | 0.070 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".