Sample Ascertainment and Recruitment Sources in the Accelerating Medicines Partnership Schizophrenia Program
Bibliographic record
Abstract
Background: This paper presents the recruitment sources of clinical high-risk (CHR) and community controls (CC) from the Accelerating Medicines Partnership Schizophrenia (AMP SCZ) program, which aims to study various clinical variables and biomarkers in 2040 CHR and 652 CC participants. Methods: A total of 1640 CHR and 514 CC had recruitment source data. The Positive Symptoms and Diagnostic Criteria for the Comprehensive Assessment of At-Risk Mental States Harmonized with the SIPS was utilized to assess CHR criteria and severity of attenuated psychotic symptoms (APSs), and the Global Functioning: Social Scale was used for social functioning. Participants were recruited through various methods, including referrals from healthcare providers, schools, and community agencies, and self-referrals via outreach efforts and advertising. Results: Participants were recruited from 13 different sources, with self-referral being the most common for both CHR and CC. Other notable sources included child and youth services and psychiatric hospitals and departments. Regional differences in recruitment patterns were observed across continents. Differences in age, APS, and social functioning for CHR participants were examined in the top 5 recruitment sources. Overall, self-referred individuals were typically older, with less severe APS and higher levels of functioning, whereas those from adult community mental health services had poorer functioning and more severe APS. The remaining recruitment groups fell between these 2 extremes. Conclusion: This paper highlights the diverse recruitment sources for the AMP SCZ program. Self-referral was a significant source, particularly in North America, reflecting changing help-seeking behaviors influenced by the internet and social media. The findings underscore the importance of understanding recruitment sources to optimize future CHR research.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".