Fidelity scales and performance measures to support implementation and quality assurance for first episode psychosis services
Bibliographic record
Abstract
AIM: The purpose of this paper is to review fidelity and outcome measures which can be used to support broad implementation of first episode psychosis services and ensure quality of existing services. First episode psychosis services use a combination of evidence-based practices to improve the outcome of a first episode of psychosis and the early stages of schizophrenia. Now that there is an established international evidence base to show that they are effective, efforts are being made to make such services widely available as a routine part of health care. METHODS: We provide an overview of the literature from the perspective of an expert task force that was commissioned to report to the board of the International Early Psychosis Association IEPA. First, we examined the evidence-based components that underpin first episode psychosis services and identified common elements. Next, we reviewed the availability of fidelity measures and outcome indicators, finally we reviewed how broadly these services are delivered internationally, and the barriers to ensuring broad access to quality services. RESULTS: There is a growing consensus about the elements required to deliver effective services. Fidelity scales and performance measures are available to assess quality, access, and outcome. First episode psychosis services are variably offered in high-income countries and rarely with attention to access and quality of services. Several strategies to promote implementation are identified. CONCLUSIONS: Fidelity scales and outcome measure are valuable resources to support widespread implementation and quality assurance for first episode psychosis services.
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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 | 0.043 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".