High School Mental Health Survey: Assessment of a Mental Health Screen
Bibliographic record
Abstract
OBJECTIVE: To estimate the size of the population in need of psychiatric diagnostic assessment, based on the 12-month period prevalence of child and adolescent psychiatric disorders in one local high school, and to evaluate the validity of the Diagnostic Interview Schedule for Children Predictive Scales 8 (DPS-8) screen against the National Institute of Mental Health-Diagnostic Interview Schedule for Children-IV (NIMH-DISC-IV) for any diagnosis. METHOD: This 2-stage cross-sectional study included high school students aged 13 to 19 years. In the first stage, we administered the DPS-8 screen. In the second stage, we used the NIMH-DISC-IV. Prevalence and validity were estimated using the Bayesian formula owing to the unequal subsample fractions in the second stage. RESULTS: A total of 222 students participated in the first stage Screen. Of these, 153 completed the second stage NIMH-DISC-IV. In this sample, the prevalence for anxiety disorders was 17%, behaviour disorders was 11%, and depressive disorders was 1%. The overall prevalence of any one of these disorders was 29%. The sensitivity of the DPS-8 screen was 53.4%, specificity was 87.6%, the positive predictive value was 63.8%, and th negative predictive value was 82%. The overall accuracy of the screen was 77%. CONCLUSION: This study highlights some of the difficulties in conducting psychiatric research in a high school population. Despite the difficulties, the DPS-8 screen, if used judiciously by school counsellors, may be helpful in identifying students needing further comprehensive psychiatric assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".