Elevated defeatist performance beliefs predict state increases in negative symptoms in daily life in clinical high-risk for psychosis youth: implications for mobile health treatments
Bibliographic record
Abstract
BACKGROUND: Negative symptoms are a strong predictor of conversion to a formal psychotic disorder in youth at clinical high-risk for developing psychosis (CHR). Identification of temporally precise mechanisms underlying increases in negative symptoms could enhance early intervention and specifically support the utility of mobile health treatments. Guided by Cognitive Behavioral models of psychopathology, we examine whether a core type of biased thinking-defeatist performance beliefs (DPB)-is a real-world mechanism of negative symptoms as well as a secondary symptom that is common in CHR youth: depressed mood. METHODS: CHR youth (n = 119) and healthy control (CN; 59) subjects completed ecological momentary assessment surveys assessing DPB, negative symptoms, and depressed mood for six days. RESULTS: CHR youth reported elevated DPB in daily life compared to CN. Greater DPB were associated with greater concurrent negative symptoms and depressed mood in daily life. Time-lagged analyses demonstrated that increased DPB at time t led to elevations in negative symptoms and depressed mood at t + 1 above and beyond the effects of the respective symptom at time t; DPB also varied across time of day, study day, day of the week, activity context, and social partners. CONCLUSIONS: DPB may be a promising shared mechanism contributing to negative symptoms and depressed mood in CHR youth in their daily life. Findings also provide proof-of-concept support for the utility of mobile health treatments targeting DPB by identifying key moments where DPB fluctuate in CHR youths' everyday environments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".