Achievement and Mental Well-Being in Post-Secondary Education (PSE) Students with Attention Deficit Hyperactivity Disorder (ADHD)
Bibliographic record
Abstract
Disproportionately few young adults with Attention Deficit Hyperactivity Disorder (ADHD) are admitted to post-secondary education. If admitted, they are more likely to withdraw from classes or fail to graduate, relative to post-secondary education (PSE) students without ADHD. They are reported to have lower levels of academic self-efficacy, confidence, and competence levels, which ultimately affect their overall achievement and Mental Well-being. Recommended treatments for people with ADHD typically include medication, accommodations to their learning environment, cognitive behavioural therapy, and social supports. In this study, 96 PSE students attending undergraduate and graduate programs at a Canadian university completed surveys to gauge their Mental Well-being, to estimate their current achievement (GPA), and the extent of their ADHD symptoms. They also completed open-ended questions about their physical activity levels, leisure activities, social supports, and coping strategies. Of the 96 participants, 75 responded to the surveys after the start of the COVID-19 pandemic lockdowns. The results showed the effects of physical activity, social supports and coping strategies, and a negative correlation of self-reported symptomology, on the Mental Well-being and achievement scores of the participants. The responses of the majority of those who completed the survey during the COVID-19 pandemic lockdown also demonstrated adaptations and coping strategies that were strength-based, and that indicated resilience and self-determination. The findings suggest that more emphasis should be placed on supporting the self-determination of this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".