Neurodiversity, Minority Status, and Mental Health: A Quantitative Study on the Experiences of Culturally Diverse University Students in Canada
Bibliographic record
Abstract
Background: There is community knowledge shared among autistic and other neurodivergent (ND) communities that ND individuals are more likely to share other non-normative identities, along with research demonstrating that ND individuals have worse mental health outcomes than neurotypicals (NTs). The purpose of this study is to assess how co-occurrence of neurodivergence with other minority statuses impacts distress and discrimination among students in a large university student sample ( N = 2206) in Canada and to examine representation of ND students across different identities and minority groups. Methods: Participants were recruited to an online survey and coded based on reported gender, sexuality, racial and linguistic groups, citizenship status, relationship preferences, religiosity, socioeducational profile, and neurodivergencies. Psychological distress was assessed using the Depression, Anxiety, and Stress Scale (DASS-21), and experience of discrimination was assessed using the Everyday Discrimination Scale (EDS). Analyses of variance were conducted to examine the differences in DASS-21 and EDS scores between ND and non-ND groups across different groups. Results: ND participants were more likely to share Two-Spirit, lesbian, gay, bisexual, transgender, queer, questioning, and other nonheterosexual and gender-expansive identities and to engage in nonconventional relationship dynamics, academic trajectories, and spiritual paths. White participants were proportionally more represented among NDs than non-White participants, which might reflect cultural and systemic factors. ND participants had higher scores in all subscales of the DASS-21 but not the EDS. Main and interaction effects in DASS-21 scores were observed between neurodivergence and gender, racial group, and income, and main effects were observed in nonheterosexual participants. Main and interaction effects were found between neurodivergence and racial and linguistic minority statuses in discrimination scores. Conclusion: Results of this study reflect the community-based understanding that ND individuals are more likely to share other non-normative identities and enlighten the impact of race, gender, and linguistic status on the mental health of ND university students. Our findings endorse a culturally sensitive approach for supporting neurominoritized students.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".