Development of the Clinical High Risk for Psychosis Services Fidelity Scale (CHRPS-FS) for Team-Based Care
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop and pilot the Clinical High Risk for Psychosis Services Fidelity Scale (CHRPS-FS). METHODS: A literature review was conducted to identify evidence-based treatments for individuals at clinical high risk for psychosis (CHRP). These findings were compared with the First-Episode Psychosis Services Fidelity Scale (FEPS-FS). Common items were retained, and others were added, modified, or deleted. Next, the Delphi process was conducted with 17 clinical and academic experts in CHRP care to determine consensus on the importance and validity of each item. Concurrently, the preliminary tool was piloted in eight coordinated specialty care (CSC) clinics serving individuals with CHRP. RESULTS: The literature review identified two components of CHRP care that were not detailed in the FEPS-FS and were added to the CHRPS-FS; furthermore, one FEPS-FS item was modified and six were removed. In the Delphi process, clinical and academic experts achieved a consensus of >80% in two rounds, with some changes in item wording and the addition of one item (stepped care approach). A CHRPS-FS assessment was successfully piloted in eight CSC clinics. The mean CHRPS-FS rating score was 3.96 (range 3.75-4.23), and the median proportion of items rated at good to high fidelity was 72% (range 66%-78%). CONCLUSIONS: The CHRP-FS is feasible to implement, has face validity based on expert consensus, can be completed in conjunction with a FEPS-FS assessment or alone, and captures variability across programs. The CHRPS-FS measures service delivery and is suitable for clinical trials, learning health care systems, and quality improvement efforts.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".