The performance of the K6 scale in a large school sample: A follow-up study evaluating measurement invariance on the Idaho Youth Prevention Survey.
Bibliographic record
Abstract
Since 2013, Idaho has been building capacity and infrastructure through the Strategic Prevention Framework State Incentive Grant to prevent substance abuse and related problems, namely psychiatric morbidity. As this federal initiative requires states to engage in data-driven strategic planning at the state and community levels, clinically validated instruments are particularly valuable in the context of school surveys that have limited space and require timely administration. Thus, the K6 scale was included on the 2014 Idaho Youth Prevention Survey as a measure of nonspecific psychological distress. To verify the unidimensional structure of the K6, principal axis and confirmatory factor analyses were performed in a school-based sample of Idaho students (n = 12,150). A series of multigroup confirmatory factor analyses were then performed to evaluate measurement invariance across gender, age, and race. Overall, the prevalence of serious psychological distress in the past 30 days was 17.2% in Idaho. Factor analyses confirmed the 1-factor solution of the K6. Four levels of measurement invariance were demonstrated across gender, age, and race. Together, these results further illustrate the construct validity of the K6 for use in adolescent populations. Other states are encouraged to include the K6 on their school surveys to facilitate policy planning and resource allocation as well as generate cross-state comparisons. (PsycINFO Database Record
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".