Job Design for Neurodiversity in the Workplace: A Review and a Roadmap for Future Research
Bibliographic record
Abstract
How can HR improve the work experience of neurodiverse employees? To answer this question, we conducted an in-depth review of the existing research on the topic from 1998, the year the term neurodiversity was coined, to 2023, the year of this writing. Based on our review of 34 papers, we build upon the job design research to delineate features of inclusive jobs across the relational, physical, and task dimensions of one’s work. We argue that while universal design (i.e., design with all abilities and diversity in mind) has been proposed as a solution toward inclusion of neurodiverse employees, its implementation in practice may lead to suboptimal accessibility as it may overlook the relational underpinning of one’s work and underestimate the role of disability identity and agency. Instead, we suggest a range of choices that enables us to theorize how job design can empower neurodiverse employees. This spectrum encompasses continua from universal to particular job design, self-crafted to supported job design, and dynamic to static job design. Our approach advances theorizing on inclusive job design and offers viable suggestions for HR professionals to demonstrate ways that job design can improve the work experience and success of neurodiverse employees. By embracing this approach, organizations can tailor their strategies to accommodate the unique strengths and preferences of neurodiverse employees, fostering a more inclusive work environment.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".