Attention-deficit/hyperactivity disorder in postsecondary students
Bibliographic record
Abstract
Kevin Nugent,1 Wallace Smart2,3 1Kinark Child and Family Services, Trent University and Sir Sanford Fleming College, Peterborough, ON, Canada; 2Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada; 3University of Lethbridge Health Centre, Lethbridge, AB, Canada Abstract: A PubMed review was conducted for papers reporting on attention-deficit/ hyperactivity disorder (ADHD) in postsecondary students. The review was performed in order to determine the prevalence and symptomatology of ADHD in postsecondary students, to examine its effects on academic achievement, and discuss appropriate management. The prevalence of ADHD symptoms among postsecondary students ranges from 2% to 12%. Students with ADHD have lower grade point averages and are more likely to withdraw from courses, to indulge in risky behaviors, and to have other psychiatric comorbidities than their non-ADHD peers. Ensuring that students with ADHD receive appropriate support requires documented evidence of impairment to academic and day-to-day functioning. In adults with ADHD, stimulants improve concentration and attention, although improved academic productivity remains to be demonstrated. ADHD negatively impacts academic performance in students and increases the likelihood of drug and alcohol problems. Affected students may therefore benefit from disability support services, academic accommodations, and pharmacological treatment. Keywords: adults, academic performance, stimulants, treatment
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".