Psychometric properties of the Mental Health and Social Inadaptation Assessment for Adolescents (MIA) in a population‐based sample
Bibliographic record
Abstract
We report on the psychometric properties of the Mental Health and Social Inadaptation Assessment for Adolescents (MIA), a self-report instrument for quantifying the frequency of mental health and psychosocial adaptation problems using a dimensional approach and based on the DSM-5. The instrument includes 113 questions, takes 20-25 minutes to answer, and covers the past 12 months. A population-based cohort of adolescents (n = 1443, age = 15 years; 48% males) rated the frequency at which they experienced symptoms of Attention Deficit Hyperactivity Disorder (ADHD), Conduct Disorder, Oppositional Defiant Disorder, Depression, Generalized Anxiety, Social Phobia, Eating Disorders (i.e. DSM disorders), Self-harm, Delinquency, Psychopathy as well as social adaptation problems (e.g. aggression). They also rated interference with functioning in four contexts (family, friends, school, daily life). Reliability analyses indicated good to excellent internal consistency for most scales (alpha = 0.70-0.97) except Psychopathy (alpha = 0.46). The hypothesized structure of the instrument showed acceptable fit according to confirmatory factor analysis (CFA) [Chi-square (4155) = 9776.2, p = 0.000; Chi-square/DF = 2.35; root mean square error of approximation (RMSEA) = 0.031; Comparative Fit Index (CFI) = 0.864], and good convergent and discriminant validity according to multitrait-multimethods analysis. This initial study showed adequate internal validity and reliability of the MIA. Our findings open the way for further studies investigating other validity aspects, which are necessary before recommending the wide use of the MIA in research and clinical settings.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".