Shift in sex and age of individuals at a clinical high risk (CHR) for psychosis: relation to differences in recruitment methods and effect on sample characteristics
Bibliographic record
Abstract
Historically, large samples of individuals at clinical high risk (CHR) for psychosis have mirrored overt psychotic disorders in both sex (predominantly male) and age representation (adolescent to early adulthood onset). We report on a recent CHR sample suggesting a shift in these distributions and explore contributing factors and clinical implications. We hypothesized that demographic differences would be related to recruitment sources and that age, sex, and recruitment sources would be related to baseline clinical profiles. Baseline data were included from the recent computerized assessment of psychosis risk (CAPR) study and the second and third waves of the North American Prodrome Longitudinal Study (NAPLS-2 and 3). Hierarchical regression was used to examine differences in sex, age, and recruitment sources between samples and relationships with clinical characteristics. Univariate analyses revealed a significant shift to female predominance, older age, and a change in recruitment source from NAPLS to CAPR. Multivariate analyses indicated that between-study differences in sex and age were conditional on recruitment source, with the apparent study effect driven by differences in the non-self-referred groups. More than 60% of participants recruited through internet self-referrals were female across samples. Clinical heterogeneity was partly related to age, sex, and recruitment source differences. Internet-based self-referrals were older and showed less severe negative symptoms, disorganization, and general symptoms and higher role functioning than non-self-referred participants. Findings highlight the importance of recruitment sources for CHR sample characteristics. Recruitment source effects, including those from internet sources, should be investigated in other CHR samples.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".