Employers and policy makers can make a difference to the employment of persons with disabilities
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to discuss what employers and policy makers can do to promote employment success for persons with disabilities. A study carried out in Rehabilitation Science at McMaster University, Ontario, Canada identified a number of themes, definitions of success and recommendations for change. METHOD: This article is a descriptive review of the study outcomes as well as a discussion of how the literature contributes to the position that employers and policy makers can do more to ensure that persons with disabilities achieve success in paid employment. RESULTS: The author proposes strategies that employers and policy makers can use that have been recommended both in the study and other more recent documents. CONCLUSIONS: Despite attempts to move the employment agenda for persons with disabilities forward, the results in both North America and Europe appear to be dismally low. There is evidence to suggest that employer activity in this regard is still minimal and that policy makers are not working together to ensure that there are opportunities for this population to succeed. Professionals working in this field need to be more actively involved with both employers and policy makers in order for the environment to change in a significant way.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".