Persons with invisible disabilities and workplace accommodation: Findings from a scoping literature review
Bibliographic record
Abstract
BACKGROUND: Invisible disabilities refer to a range of mental and physical disabilities that, like visible impairments, vary in their origins, degree of severity and in whether they are episodic or permanent. Much of the mainstream literature on employment and disability does not consider the question of a person disclosing their hidden disability to an employer. While disclosure is the route to a workplace accommodation process and can be in the best interest of the employee with a disability, it is a highly risky decision to disclose with numerous potential disadvantages along with advantages. The resulting situation is the predicament of disclosure for employees with invisible disabilities. OBJECTIVE: Employers can create a workplace culture that encourages disclosure by people with invisible disabilities by being clear about the competencies required for a job; giving as much information, in accessible formats, as possible in advance; and, in recruitment and selection processes, allowing opportunities for the individual to disclose. CONCLUSION: Many workplace accommodations for people with visible or invisible disabilities are actually about managing effectively rather than making exceptions: about having clear expectations, open communications and inclusive practices.
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 | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.033 | 0.038 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".