Supporting Transition to the Workforce for Neurodivergent Learners: Insights from a Canadian Studyon the Neuroinclusivity of Post-Secondary Education
Bibliographic record
Abstract
This article offers evidence-based recommendations to enhance the neuroinclusivity of post-secondary career education and practices for career educators and student support professionals. The authors share insights from an applied research study that employed a mixed method design that included a national survey of neurodivergent post-secondary students and recent graduates (n=400) and 78 in-depth interviews with neurodivergent students and recent graduates (n=45) and staff and leaders working in accessibility services (n=33). Findings revealed that neurodivergent post-secondary students encountered significant barriers to post-secondary education, which impact graduation rates and successful workforce transitions. Reduction of stigma, improved access to tailored supports, and neuroaffirming approaches to supporting students with potential transition challenges such as employment searching, disclosure, and accommodations were identified as enablers for success. This article presents recommendations from the most comprehensive national data set on neuroinclusivity in Canadian post-secondary education. This article offers actionable recommendations for career educators to use strength-based approaches and reduce employment barriers for neurodivergent individuals.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".