The german version of the conners adult ADHD rating scales (CAARS)
Bibliographic record
Abstract
Introduction Instruments for diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) in childhood are well validated and reliable, but psychometric assessment of ADHD in adults remains problematic. To date the Conners Adult ADHD Rating Scales (CAARS) are frequently used in the assessment of ADHD. Objectives The CAARS were translated into German and a series of studies planned to establish psychometric properties of the CAARS-self and -observer rating scales. Aims To evaluate the German version of the CAARS. Methods We recruited 847 healthy German subjects and 466 adult ADHD patients to fill out the CAARS-self report and questions on socio-demographic variables. Additionally, 896 CAARS-observer reports were filled out by significant others and clinical experts. Factor analyses were conducted to obtain factor structure and to replicate the structure of the original American-model. Comparisons between patients and controls, and analyses on influences of gender, age, and education level were calculated. Additional analyses established psychometric properties. Results Confirmative factor analysis based on the original American-model showed a high model-fit for both the German healthy control and the adult ADHD patient sample. Analyses of normative data showed significant influences of age, gender, and education level on the emerging subscales for the control sample only. Differences on all subscales were highly significant between patients and controls. Test-, test-retest-reliability was very high, and criterion validity could be established with DSM-IV based clinical interviews. Sensitivity and specificity ratings are overall very satisfying. Conclusion The German version of the CAARS is a cross-culturally valid instrument for the assessment of adult ADHD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".