Attention-deficit hyperactivity disorder and addictions (substance and behavioral): Prevalence and characteristics in a multicenter study in France
Why this work is in the frame
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Bibliographic record
Abstract
AIM: The aim of this study is to determine the possible links between attention-deficit hyperactivity disorder (ADHD) and the presence of concomitant addictions with or without substance use in a French student population. MEASURES: A battery of questionnaire measuring socioeconomic characteristics, university curriculum, ADHD (Wender Utah Rating Scale and Adult ADHD Self-Report Scale), substance consumptions (alcohol, tobacco, and cannabis), and behavioral addictions [(eating disorders (SCOFF)], Internet addiction (Internet Addiction Test), food addiction (Yale Food Addiction Scale), compulsive buying (Echeburua's), and problem gambling (The Canadian Problem Gambling Index)] and measures of physical activity (Godin's Leisure Time Exercise Questionnaire) was filled up by university students in Rouen and Nanterre in France. RESULTS: A total of 1,517 students were included (472 from Paris Nanterre and 1,042 from Rouen). The mean age was 20.6 years (SD = 3.6) and the sex ratio male to female was 0.46. The prevalence of ADHD among the students (current ADHD with a history of ADHD in childhood) was 5.6%. A quarter (25.7%) of students had already repeated their university curriculum, compared to 42.2% among the students with ADHD. Students with possible ADHD had repeated classes more often and believed to have a lower academic level than the students without ADHD. Significant differences were found as students with ADHD were less likely to succeed in their studies (repeated classes more often) than non-ADHD students, and considered their academic level to be lower. They also had significantly higher scores on substance (alcohol, cannabis, and tobacco) as well as behavioral addictions (gambling, compulsive buying disorder, eating disorders, and Internet addiction). CONCLUSION: It seems essential to determine students' problems and propose interventions adapted to students' needs, in order to reduce the negative impact on their future academic and global successes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it