Predicting psychosis-spectrum diagnoses in adulthood from social behaviors and neighborhood contexts in childhood
Bibliographic record
Abstract
Research showing that risk for schizophrenia, bipolar disorder with psychosis, and other psychosis-spectrum diagnoses in adulthood is multidetermined has underscored the necessity of studying the additive and interactive factors in childhood that precede and predict future disorders. In this study, risk for the development of psychosis-spectrum disorders was examined in a 2-generation, 30-year prospective longitudinal study of 3,905 urban families against a sociocultural backdrop of changing economic and social conditions. Peer nominations of aggression, withdrawal, and likeability and national census information on neighborhood-level socioeconomic disadvantage in childhood, as well as changes in neighborhood socioeconomic conditions over the lifespan, were examined as predictors of diagnoses of schizophrenia, bipolar disorder, and other psychosis-spectrum disorders in adulthood relative to developing only nonpsychotic disorders or no psychiatric disorders. Individuals who were both highly aggressive and highly withdrawn were at greater risk for other psychosis-spectrum diagnoses when they experienced greater neighborhood disadvantage in childhood or worsening neighborhood conditions over maturation. Males who were highly aggressive but low on withdrawal were at greater risk for schizophrenia diagnoses. Childhood neighborhood disadvantage predicted both schizophrenia and bipolar diagnoses, regardless of childhood social behavior. Results provided strong support for multiple-domain models of psychopathology, and suggest that universal preventive interventions and social policies aimed at improving neighborhood conditions may be particularly important for decreasing the prevalence of psychosis-spectrum diagnoses in the future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".