Performance Measures for Early Psychosis Treatment Services
Bibliographic record
Abstract
OBJECTIVE: This study examined the feasibility of identifying performance measures for early psychosis treatment services and obtaining consensus for these measures. The requirements of the study were that the processes used to identify measures and gain consensus should be comprehensive, be reproducible, and reflect the perspective of multiple stakeholders in Canada. METHODS: The study was conducted in two stages. First a literature review was performed to gather articles published from 1995 to July 2002, and experts were consulted to determine performance measures. Second, a consensus-building technique, the Delphi process, was used with nominated participants from seven groups of stakeholders. Twenty stakeholders participated in three rounds of questionnaires. The degree of consensus achieved by the Delphi process was assessed by calculating the semi-interquartile range for each measure. RESULTS: Seventy-three performance measures were identified from the literature review and consultation with experts. The Delphi method reduced the list to 24 measures rated as essential. This approach proved to be both feasible and cost-effective. CONCLUSIONS: Despite the diversity in the backgrounds of the stakeholder groups, the Delphi technique was effective in moving participants' ratings toward consensus through successive questionnaire rounds. The resulting measures reflected the interests of all stakeholders.
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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 | 0.066 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".