Improving Autistic Experiences in the Workplace: Key Factors and Actionable Steps
Bibliographic record
Abstract
Autistic adults have higher rates of unemployment and underemployment than non-autistic adults with and without disabilities. While previous work has highlighted factors specific to individuals and/or job sectors that serve as barriers or facilitators to autistic employment, the question of how to modify the workplace to best support autistic people remains under-researched. The present study utilized an ecological framework to investigate what workplace factors can be modified to improve autistic experiences and how these modifications may be enacted across different levels of workplace ecosystem to promote autistic success. Autistic participants (N = 85) across employment sectors provided quantitative ratings and written descriptions of positive and negative factors related to their workplace experiences. Quantitative and qualitative analyses were used to examine which factors and overarching principles most impact employment. Actionable strategies to modify these factors were derived from participant responses and validated by autistic collaborators and neuroinclusion experts. On average, participants rated task training as having the most positive, and mental health as having the most negative, impact on their employment. Participants described four themes (acceptance, communication, autonomy, accommodations) that can be embedded in the work environment to improve experiences. Steps to improve autistic employment that can be enacted by stakeholders across levels of the workplace experiences are provided. Autistic adults face multifaceted barriers to employment across levels of the workplace. Modifying the workplace itself, across multiple levels and stakeholders, may serve to improve autistic employment outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".