DSM‐IV Internal Construct Validity: When a Taxonomy Meets Data
Bibliographic record
Abstract
The use of DSM-IV based questionnaires in child psychopathology is on the increase. The internal construct validity of a DSM-IV based model of ADHD, CD, ODD, Generalised Anxiety, and Depression was investigated in 11 samples by confirmatory factor analysis. The factorial structure of these syndrome dimensions was supported by the data. However, the model did not meet absolute standards of good model fit. Two sources of error are discussed in detail: multidimensionality of syndrome scales, and the presence of many symptoms that are diagnostically ambiguous with regard to the targeted syndrome dimension. It is argued that measurement precision may be increased by more careful operationalisation of the symptoms in the questionnaire. Additional approaches towards improved conceptualisation of DSM-IV are briefly discussed. A sharper DSM-IV model may improve the accuracy of inferences based on scale scores and provide more precise research findings with regard to relations with variables external to the taxonomy.
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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 | 0.156 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".