French version of the strengths and weaknesses of ADHD symptoms and normal behaviors (SWAN-F) questionnaire.
Bibliographic record
Abstract
OBJECTIVE: To evaluate internal and external consistency of a French adaptation of the SWAN (a 7-point rating strength-based scale, from far below to far above average) and its accuracy as a diagnostic test among children with Attention Deficit/Hyperactivity Disorder (ADHD). METHOD: Parents of 88 children referred for ADHD were interviewed using the SWAN-F, a structured interview (DISC-4.0) and the Conners' Rating Scale. Internal consistency and divergent and convergent validity of the SWAN-F were examined using the DISC-4.0 and Conners' Rating Scales as reference standards for four dimensions: Inattention, Hyperactivity/Impulsivity, ADHD, Oppositional Defiant Disorder. RESULTS: The internal consistency of SWAN-F was within acceptable ranges for all dimensions (Cronbach's alpha greater than 0.80). Scores of the SWAN-F subscales were strongly associated with the DISC-4.0 diagnostic assignments and Conners' Rating Scales, following logical patterns of correspondence between diagnoses. Its accuracy as a diagnostic test was comparable to Conners' Rating Scale, with a lower rate of false positives. CONCLUSIONS: The information gathered with the SWAN-F is compatible with that obtained using the DISC-4.0 and Conners' Rating Scale. Strength-based rating scales have the potential to evaluate the normal distribution of behaviors and to provide reliable cut-off defining abnormal behavior.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".