The Impact of Gender, Accommodations, and Disability on the Academic Performance of Canadian University Students with LD and/or ADHD
Bibliographic record
Abstract
Academic accommodations for students experiencing disabilities are increasingly available at postsecondary institutions. More studies of the efficacy of accommodations for student success are warranted, however. Given the increased gender gap in university participation, more focus on the unique impact of gender is also needed. Using a sample of students registered with Access and Inclusion Services with learning disabilities (LD), attention–deficit/hyperactivity disorder (ADHD), and combined LD/ADHD at a Canadian undergraduate university ( N = 661), we explored the impact of gender on academic performance and accommodation usage. Next, we examined how gender intersected with the impact of academic accommodations on academic performance. Women, on average, demonstrated better academic performance. Academic strategies and assistive technologies were not associated with higher academic performance. However, testing accommodations (extended time and environmental accommodations) were positively associated with academic performance for men with LD or ADHD, but not for the combined group LD/ADHD. For the former two, the more tests accommodated, the higher the academic performance. Furthermore, this gender association was most prominent for students experiencing ADHD. Interpretations and policy recommendations related to these findings are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".