Essential Evidence-Based Components of First-Episode Psychosis Services
Bibliographic record
Abstract
OBJECTIVE The purpose of this study was to identify essential evidence-based components of first-episode psychosis services. METHODS The study was conducted in two stages. In the first stage a systematic review of both peer-reviewed and gray literature (January 1980 to April 2010) was conducted. Databases searched included MEDLINE, PsycINFO, and EMBASE. In the second stage, a consensus-building technique, the Delphi, was used with an international panel of experts. The panelists were presented the evidence-based components identified in the review, together with the level of supporting evidence for each component. They rated the importance of each component on a 5-point scale. A score of 5 was required to determine that a component was essential. RESULTS The review identified 1,020 citations; abstracts were reviewed for relevance. A total of 280 peer-reviewed articles met criteria for relevance. Two researchers independently reviewed these articles and identified 75 unique service components. Each component was assigned a level of supporting evidence. Twenty-seven experts completed the first Delphi round, of whom 23 participated in the second. Consensus was achieved in two rounds, with 32 components rated as essential. CONCLUSIONS The two-step process yielded a manageable list of 32 evidence-based components of first-episode psychosis services. Given the proliferation of such services and the absence of an evidence-based fidelity scale, this list can form a foundation for developing a fidelity scale for such services. It may also be helpful to funders and providers as a summary of essential 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 | 0.075 | 0.279 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".