Youth Experience Tracker Instrument: A self‐report measure of developmental antecedents to severe mental illness
Bibliographic record
Abstract
Abstract Aim We sought to examine the structure, internal consistency, convergent and criterion validity of the Youth Experience Tracker Instrument (YETI), a new brief self‐report measure designed to facilitate early identification of risk for severe forms of mental illness, including major depressive disorder, bipolar disorder, and schizophrenia. Methods We collected 716 YETIs from 315 individuals aged 8 to 27 with and without familial risk of severe mental illness. The YETI measures six developmental antecedents that precede and predict serious forms of mental illness: affective lability, anxiety, basic symptoms, depressive symptoms, psychotic‐like experiences, and sleep. A battery of concurrent questionnaires and interviews measured the same constructs. Results The best‐fitting bifactor model supported the validity of both total score and antecedent‐specific subscales. Internal consistency was high for the total score ( ω = 0.94) and subscales ( ω = 0.80‐0.92; ρ = 0.72). The total score captured the majority of information from the 26 YETI items (hierarchical omega ω h = 0.74). Correlations of YETI subscales with established measures of the same constructs ( r = 0.45‐0.80) suggested adequate convergent validity. We propose cut‐offs with high negative predictive values to facilitate efficient risk screening. Conclusion The YETI, a brief self‐report measure of antecedents, provides an alternative to using multiple longer instruments. Future research may examine the predictive validity of the YETI for the onset of major mood and psychotic disorders.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".