Protective Factors Predict Resilient Outcomes in Clinical High-Risk Youth with the Highest Individualized Psychosis Risk Scores
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Studying individuals at Clinical High Risk (CHR) for psychosis provides an opportunity to examine protective factors that predict resilient outcomes. Here, we present a model for the study of protective factors in CHR participants at the very highest risk for psychotic conversion based on the Psychosis Risk Calculator. STUDY DESIGN: CHR participants (N = 572) from NAPLS3 were assessed on the Risk Calculator. Those who scored in the top half of the distribution and had 2 years of follow-up (N = 136) were divided into those who did not convert to psychosis (resilient, N = 90) and those who did (nonresilient, N = 46). Groups were compared based on candidate protective factors that were not part of the Risk Calculator. Better functional outcome was also examined as an outcome measure of resiliency. Study Results: Exploratory analyses suggest that Hispanic heritage, social engagement, desirable life experiences, premorbid functioning and IQ are all potential protective factors that predict resilient outcomes. Reduced startle reactivity, brain area and volume were also associated with greater resilience. CONCLUSIONS: The primary focus of CHR research has been the risk and prediction of psychosis, while less is known about protective factors. Clearly, a supportive childhood environment, positive experiences, and educational enrichment may contribute to better premorbid functioning and brain development, which in turn contribute to more resilient outcomes. Therapies focused on enhancing protective factors in the CHR population are logical preventive interventions that may benefit this vulnerable population. Future CHR research might use similar models to develop a "protective index" to predict resilient outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".