Does an integrated outreach intervention targeting multiple stages of early psychosis improve the identification of individuals at clinical high risk?
Bibliographic record
Abstract
AIMS: To explore the impact of a targeted case identification intervention, with training and education regarding first-episode psychosis and clinical high-risk syndromes, on the referral and identification of those at high risk. METHODS: Using a historical control design, referral information from pre-intervention and post-intervention periods was collected via administrative data and clinician notes from a catchment-based early psychosis service. RESULTS: A significant increase in the number of referrals sent to the service's clinical high-risk unit was observed following the intervention (P = 0.01). The proportion of referrals eligible was significantly higher post-intervention (P = 0.03), with the majority (26/44, 59.1%) referred via the first-episode psychosis service unit. CONCLUSIONS: An integrated outreach intervention for both first-episode psychosis and the clinical high-risk state was effective in increasing referrals of eligible cases to the service's at-risk unit. Rather than being stage-specific, targeted case identification strategies and service integration should span across the early stages of psychosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".