Addressing Barriers in the University Campus Environment for Neurodivergent Students
Bibliographic record
Abstract
About 15% of the global population is considered neurodivergent (having different ways of sensory processing than what are perceived as neurotypical persons). Persons with neurodivergence typically include those with autism, attention-deficit hyperactivity disorder (ADHD), and Down Syndrome. Studies have shown that many neurodivergent persons experience sensory processing disorder (SPD). Noise, lighting, temperature, and aesthetics are some factors that can significantly impact the quality of interaction with the built environment for these individuals. A significantly lower proportion of youths with SPD enter higher educational institutions (HEI), hindered in part by physical design on university campuses. Universities in Canada are now mandated to address barriers in the campus environments for persons with disability. However, space design needs of neurodivergent students are often overlooked. We interviewed eight neurodivergent persons with SPD (NPSPD) about their experiences as students navigating a university campus located in Halifax, Canada. We asked what specific spaces on campus pose barriers to them (to enter, traverse, and use), and how the design should be improved. The participant responses revealed many elements on campus that act as barriers-largely consistent with existing literature. However, the participants' comments illustrated more complex dynamics of these factors, which can exacerbate their stress and anxiety. Oftentimes, barriers are more to do with lack of information about the characteristics of the space prior to using it, which would otherwise allow students to plan ahead their journey to destinations and use of given spaces. A wayfinding aid that informs what to expect in spaces or pathways on campus would be a potential area for innovation, along with multiple services to comprehensively and flexibly cater to individual needs to alleviate sensory overload. Ongoing communications about barriers across campus by all users of the university campus would facilitate implementation of pragmatic solutions needed to address diverse needs existing in HEI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".