A Scoping Review of Measures Used in Early Intervention Services for Psychosis
Bibliographic record
Abstract
OBJECTIVE: The early intervention service (EIS) model for psychosis has been implemented with increasing frequency; yet, improving outcomes across domains for all patients remains challenging. Measurement-based care can strengthen outcomes by optimizing interventions and promoting alignment with standards, but it is still not widely deployed in EIS. The authors conducted a scoping review by systematically identifying and synthesizing measures used in EIS related to purpose (i.e., to assess patients, families, and programs), domains (e.g., symptoms, quality of life), and reporting perspectives (of patients, families, and clinicians). METHODS: EMBASE, MEDLINE, PsycINFO, CINAHL, and Cochrane Library databases were searched for pertinent literature published between 2000 and 2020. Two reviewers independently screened titles, abstracts, and full texts and extracted data. Measures were classified as clinician-reported outcome measures (CROMs), patient-reported outcome or experience measures (PROMs/PREMs), or family-reported outcome or experience measures (FROMs/FREMs). RESULTS: In total, 172 measures of 27 domains were identified from 115 articles. Nineteen measures had been used to assess programs on fidelity, service engagement, and satisfaction; 136 to assess patients on duration of untreated psychosis, symptoms, functioning, quality of life, and others; and 17 to assess families on coping and burden, background, and others. Sixty percent were CROMs, 30% were PROMs/PREMs, and 10% were FROMs/FREMs. CONCLUSIONS: Greater inclusion of PROMs and FROMs is needed because they align with the EIS philosophy of patient and family engagement and may improve shared decision making and outcomes. A comprehensive, meaningfully synthesized archive of measures can advance measurement-based care, services research, and data harmonization in early psychosis.
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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 | 0.070 | 0.246 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.059 | 0.062 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".