Navigating Educational Hurdles: The Impact of Attention and Hyperactivity Problems on School Absenteeism
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder that, if left undetected and untreated, can impede the life trajectory of thousands of children. Unfortunately, children and youth with this disorder often go unnoticed within the education system. Successful completion of education is critical for equipping students with ADHD with the skills to effectively manage their condition, leading to better overall life outcomes. Research has connected a lack of childhood skill development in managing ADHD symptoms with severe psychosocial outcomes such as failure in school, bleak employment outcomes, high crime and incarceration rates, and even premature death. Unfortunately, the COVID-19 pandemic has exacerbated these educational outcomes, disproportionately impacting school attendance rates among children with ADHD. This review aimed to investigate the relationship between attention and hyperactivity problems (AHP) among school-age children/youth and school absenteeism in both pre-pandemic and post-pandemic studies. The main objective was to investigate whether children with AHP were missing school at higher rates than their non-AHP peers. Through a systematic literature search across numerous databases, 1282 studies were assessed, identifying 35 studies focusing on the association between AHP and school absenteeism. Preliminary analyses revealed that students with AHP were more prone to experiencing increased school absenteeism, including cases of school refusal, avoidance, suspension, and expulsion. These findings are troubling as numerous studies have linked school absenteeism with adverse educational and psychosocial outcomes. Thus, there is an urgent need for educational and policy reforms that prioritize the support and accommodation of children with attention and hyperactivity problems.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".