The performance of the K6 Scale in a large school sample.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it