International application of standards for health care quality, access and evaluation of services for early intervention in psychotic disorders
Bibliographic record
Abstract
AIM: Standards for health care quality, access and evaluation of early intervention in psychosis services are required to assess implementation, provide accountability to service users and funders and support quality assurance. The aim of this article is to review the application of standards in Europe and North America. METHODS: Descriptive methods will be used to illustrate the organizational context in which standards are being applied and used, specific measures being applied and results so far. RESULTS: Both fidelity scales and quality indicators of health care are being used. Fidelity scales are being applied in Australia, Canada, Denmark, Italy and United States. In England, quality indicators derived from the National Institute for Health and Care Excellence guidance are being used. CONCLUSION: In the last 4 years, significant progress has been made in the development and application of measures that assess quality and access to evidence-based practices for early intervention in psychosis services. This represents an important step towards providing accountability, improving outcomes and service user experience. The methods used allow for comparison between the services that are assessed with the same methods, but there is a need to compare the different methods. Further research is also required to explore links between quality of care and outcomes for community mental health services that deliver early intervention in psychotic disorders.
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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 | 0.371 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".