An exploratory study of benefits and challenges of neurodivergent employees: roles of knowing neurodivergents and neurodiversity practices
Bibliographic record
Abstract
Purpose Neurodiversity refers to a spectrum of neurological differences. Little is known about the benefits and challenges of employing neurodivergent individuals in the retail industry and how knowing neurodivergent individuals/neurodiversity practices are linked to benefits/challenges. This study provides these insights using the lenses of the value-in-diversity perspective, stigma theory and intergroup contact theory. Design/methodology/approach Data were collected from an online survey of retail supervisors and co-workers from Australia, resulting in 502 responses from various retail organizations. Findings The findings indicate that supervisors have higher awareness of neurodiversity and perceived benefits of neurodivergent employees. Knowing neurodivergents was positively associated with perceived benefits and disclosure challenges and negatively associated with equity and inclusion challenges. Neurodiversity practices were positively associated with benefits of neurodivergent employees, negatively associated with disclosure challenges and equity and inclusion challenges in small stores, and positively associated with equity and inclusion challenges in large stores. Originality/value Current empirical research on workplace neurodiversity is scarce. This study provides pioneering evidence for awareness of workplace neurodiversity in the retail industry and the impact of knowing neurodivergent employees/neurodiversity practices on benefits and challenges. It differentiates between supervisors' and co-workers’ perceptions, highlighting the importance of exposure to information in reducing stigma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".