A framework for developing employer’s disability confidence
Bibliographic record
Abstract
Purpose Many employers lack disability confidence regarding how to include people with disabilities in the workforce, which can lead to stigma and discrimination. The purpose of this paper is to explore the concept of disability confidence from two perspectives, employers who hire people with a disability and employees with a disability. Design/methodology/approach A qualitative thematic analysis was conducted using 35 semi-structured interviews (18 employers who hire people with disabilities; 17 employees with a disability). Findings Themes included the following categories: disability discomfort (i.e. lack of experience, stigma and discrimination); reaching beyond comfort zone (i.e. disability awareness training, business case, shared lived experiences); broadened perspectives (i.e. challenging stigma and stereotypes, minimizing bias and focusing on abilities); and disability confidence (i.e. supportive and inclusive culture and leading and modeling social change). The results highlight that disability confidence among employers is critical for enhancing the social inclusion of people with disabilities. Originality/value The study addresses an important gap in the literature by developing a better understanding of the concept of disability from the perspectives of employers who hire people with disabilities and also employees with a disability.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".