The performance of the K6 Scale in a large school sample.
Bibliographic record
Abstract
Timely prevalence data of psychiatric morbidity among adolescents in small areas remains vital for mental health policy planning at the regional and local levels. Furthermore, effective regional policy planning also requires the measurement of psychiatric morbidity using clinically validated instruments. The K6 scale was therefore included on the 2012 administration of the Kentucky Incentives for Prevention Survey as a measure of serious emotional disturbance in the past 30 days. Principal axis and confirmatory factor analyses were performed to determine the unidimensional structure of the K6 in a school-based sample of Kentucky students (n = 108,736). The documented cutoff of 13 on the K6 was then used to screen Kentucky students for serious emotional disturbance, estimate the state prevalence, and define epidemiologic correlates. Overall, the K6 performed well, with factor analyses confirming the 1-factor solution of the K6. Based upon the established cutoff, the prevalence of serious emotional disturbance was 13.9% in Kentucky. Grade, gender, race and ethnicity, and family structure emerged as significant predictors in a multivariable logistic regression model. Substance abuse, antisocial behavior, role impairments, and peer victimization were significantly higher among students with a positive screen. These results indicate the K6 is particularly useful for inclusion in large epidemiologic surveys that have limited space and logistics that demand timely administration.
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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 | 0.006 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".