Engaging workers with disabilities in the financial sector: exploring promising practices through key informant interviews and a rapid literature review
Bibliographic record
Abstract
Purpose This study explores the challenges, barriers and opportunities for engaging persons with disabilities in employment in the Canadian financial sector. Design/methodology/approach We situated this research within a critical disability conceptual framework to add to existing theories of employee engagement. We conducted an exploratory qualitative study of key informant interviews of the experiences of diverse persons with disabilities in the Canadian financial sector. Findings We found that the financial sector has the potential to be a leader in the engagement of workers with disabilities. Key challenges include corporate bureaucracy and a focus on aggressive growth that perpetuates ableist norms of individualism, self-reliance and competitive achievement. Key informant interviews indicated that opportunities for engagement can be fostered by committed leadership, inclusive corporate culture, supportive management, and respectful and responsive workplace accommodations. Originality/value This article contributes to the literature on opportunities for equity, diversity and inclusion at work through a critical exploration of the challenges and promising practices associated with supporting engagement of persons with disabilities in the Canadian financial sector and beyond.
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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 | 0.065 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".