ADJUSTMENT OF GRADUATE STUDENTS WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD)
Bibliographic record
Abstract
The difficulties of undergraduate students with ADHD are widely documented (Emmers, Jansen, Petry, van der Oord, & Baeyens, 2017).This allows the development of support measures tailored to their needs.Many of these students complete their undergraduate studies and enter master's and doctoral programs.Graduate studies present additional challenges, particularly related to independent research and writing.However, few studies have explored the adjustment of students with ADHD at the graduate level, that is their ability to meet the demands of their study environment.This study aims to compare the adjustment of master's and doctoral students with ADHD (n = 16) and without ADHD.Participants completed a French translation of the Student Adaptation to College Questionnaire (Pariat, 2008, Baker & Syrik, 1999).This scale measures four dimensions of adjustment: academic, social, personal-emotional and goal commitment/institutional attachment.Students with ADHD present average scores for the overall scale and the four subscales.Moreover, their scores do not differ significantly from those of the students without ADHD.These results indicate that graduate students with ADHD may respond as well to the demands of their study environment as their peers without ADHD, despite the additional challenges associated with their diagnosis.Further studies should explore the conditions and mechanisms facilitating their adaptation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".