Understanding the Mental Health Experiences of Autistic Students in Higher Education: A Mixed Methods Approach
Bibliographic record
Abstract
Abstract Background: Mental health issues like depression and anxiety are a persistent problem among higher education students, especially students on the autism spectrum. The reasons behind autistic students’ high rates of mental health problems remain unclear, with few studies addressing this topic. However, several factors impacting mental health have been identified within autistic adults and non-autistic higher education students separately, such as perceived campus climate, internalized stigma, and camouflaging (attempts to hide one’s autism). Method: In this exploratory mixed methods study, I combined semi-structured interviews and online surveys to investigate factors associated with mental health among a sample of autistic students attending the University of Calgary (UCalgary). Quantitative analyses included collecting descriptive statistics from the survey and running correlations between mental health outcomes (e.g., stress, quality of life) and potential factors (e.g., campus climate, stigma, camouflaging), while qualitative analysis consisted of reflexive thematic analysis of interview data to understand participants’ lived experiences with mental health. Results: Based on 12 surveys and 10 interviews, several key findings emerged. First, participants reported high levels of depression, anxiety, and stress. Second, autism-specific quality of life was negatively correlated with stigma but positively correlated with camouflaging and campus climate; no other correlations between factors and mental health outcomes reached significance, although stigma and camouflaging had a strong negative correlation. Finally, three key themes were developed from the interviews: 1) autistic students have heterogeneous experiences with mental health; 2) mental health is affected by personal autism identity and societal-level messages about autism, including stigma; and 3) the UCalgary environment has both positive (e.g., providing autism-specific supports) and negative (e.g., too much sensory input) influences on mental health. Collectively, these findings support past literature and underscore the need to increase mental health support for autistic students. Conclusions and Impact: These initial findings highlight how a variety of individual and environmental factors contribute to autistic students’ mental health. Addressing these factors can create more inclusive environments at higher education institutions like UCalgary, thus fostering well-being and academic success for autistic and non-autistic students alike.
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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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".