Absenteeism In Children with ADHD Symptomology: The Roles of Parental Educational Support and Parent-Teacher Trust
Bibliographic record
Abstract
Youth with ADHD are absent from school at twice the rate of their non-ADHD peers (Niemi et al., 2022). While parental educational support benefits academic outcomes, its impact on school attendance in children with ADHD is less understood, as is the relationship between school attendance in children with ADHD and parent-teacher trust. This study investigates two questions: (1) Is parental educational support associated with school attendance among children with ADHD symptoms, and does child age moderate this? (2) Is parent-teacher trust associated with children’s school attendance, and does child age moderate this? In 2022, data were collected from 95 parents of children aged 9–14 with ADHD symptoms across Canada. Regression analyses indicated that greater parental educational support was significantly associated with fewer school absences, R² = .20, F(3, 90) = 7.36, p < .001. Moreover, moderation analysis showed that child age significantly moderated this relationship, such that the effect of parental support on reducing absences was stronger for older children, b = –.05, p = .005. Parent-teacher trust was also significantly associated with fewer absences, R² = .16, F(3, 90) = 5.68, p = .001. However, moderation analysis found that child age did not significantly moderate this relationship, b = –.06, t(94) = –0.393, p = .694. These findings suggest that both parental support for learning and strong parent-teacher trust contribute to improved school attendance among children with ADHD symptoms. Notably, the role of parental support becomes increasingly important as children age. This insight may guide educators and health professionals in fostering collaboration to reduce absenteeism in 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".