ADHD and Academic Success in University Students: The Important Role of Impaired Attention
Bibliographic record
Abstract
OBJECTIVE: To improve on several methodological issues regarding current literature investigating the relationship between ADHD symptomatology and academic success in adults and examine the relative contributions of specific dimensions of ADHD symptomatology (i.e., inattention, hyperactivity, and impulsivity) to post-secondary academic success. METHOD: A large sample of 3,688 post-secondary students were examined using a longitudinal design. The Conners' Adult ADHD Rating Scale (CAARS) was used to assess adult ADHD symptoms and academic success was assessed using students' official academic records (e.g., final GPAs and degree completion status). RESULTS: Students with greater inattention symptomatology at the start of their academic program showed consistently poorer long-term academic success (i.e., lower GPAs, higher dropout rates), regardless of gender. CONCLUSION: Inattention symptoms are the primary driver of the relationship between ADHD symptomatology and academic underachievement in adults. Post-secondary education institutions should target and prioritize educational programming for inattention symptoms of ADHD in at-risk post-secondary students.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".