Examining the Prevalence Rates, Demographic Differences, and Concurrent Validity Associated with a Universal Bidimensional Mental Health Screener for Youth in Schools
Bibliographic record
Abstract
When using a bidimensional mental health (BDMH) model, psychological distress and wellbeing are measured. This study used a mental health screening measure, with equal number of items measuring each mental health dimension (i.e., wellbeing and distress) to classify students into one of four possible mental health groups: mentally healthy (MH), mentally unhealthy (MU), symptomatic but content (SBC), and asymptomatic but discontent (ABD). First, prevalence rates for each group in a sample of youth from the 2009–10 Health Behavior in School-aged Children Survey in the United States (N = 6,345) were explored; about a quarter of the population experienced mixed mental health (i.e., SBC or ABD). The second purpose was to investigate how demographic variables (e.g., gender, ethnicity) influenced a student’s BDMH; these variables did not have a practically meaningful relationship to BDMH. The third purpose was to investigate the effect of BDMH classification (i.e., MH, MU, SBC, or ABD) on relevant student behavior variables (i.e., school performance perceptions, class climate, bullying victimization and perpetration, family support, life satisfaction, somatic symptoms, alcohol, cigarette, and marijuana use). Results indicated that MH students experienced the most advantageous, and MU students the most deleterious, concurrent outcomes. However, ABD students (not identified by a traditional screener) experienced concurrent outcomes worse than or similar to their MU peers. Taken together, the results suggest that measuring wellbeing has value-added in differentiating students with varying levels of risk, and identifying students with potential need for intervention. Implications of these results and considerations regarding measurement of psychological wellbeing in mental health screening procedures in schools are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".