Attention-deficit/hyperactivity disorder in nursing education: Implications for teaching, learning, and practice
Bibliographic record
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) remains under-recognized within nursing education despite its prevalence. ADHD is recognized as a chronic neurodevelopmental disorder. It often manifests as persistent patterns of inattention, impulsivity, and difficulties with executive functioning, which can significantly impact a student's academic and clinical performance. While clinicians typically diagnose ADHD during childhood, it sometimes continues into adulthood and is increasingly identified among college students, including those in nursing programs. Few studies have specifically explored ADHD's impact on nursing students, underscoring the need for further research and a deeper understanding of this chronic neurodevelopmental disorder. This paper examines the complex causes of ADHD and emphasizes how core symptoms affect nursing students' engagement, task completion, and clinical competence. The demanding nature of nursing education highlights the importance of executive functions in supporting self-regulation, planning, and time management. Additionally, effective teaching strategies for helping students with ADHD are outlined. Nurse educators play a critical role in promoting academic and clinical success by adopting responsive, evidence-based teaching practices that acknowledge neurodiversity.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".