Exploring Intersections of Race and Disability in the Context of Canadian Employment Support Systems Through the Experiences of Job Seekers/Workers, Employers, and Service Providers
Bibliographic record
Abstract
PURPOSE: Racism and ableism have impacts at individual and organizational levels and can produce and reproduce inequities and injustices in diverse contexts. However, their intersection remains largely unexamined in the provision of employment supports. The objective of this qualitative study is to identify barriers and facilitators within employment supports to seek strategies to improve the employment outcomes of racialized disabled job seekers and workers and address gaps faced by service providers and employers. METHODS: This study used interpretive description (Thorne S. Interpretive description: Qualitative research for applied practice; 2016.). Four racialized disabled job seekers and workers, two employers and four service providers from Canada participated in semi-structured interviews. Thematic analysis (Braun and Clarke in Qual Res Psychol 3:77-101, 2006) was used to analyze the data. FINDINGS: Five core themes were identified: (1) managing intersectional confusion; (2) employer education; (3) contextual barriers; (4) client-service provider relationships; and (5) urgency for solutions. CONCLUSION: This study provides grounding evidence about common concerns and barriers within existing employment support systems and can assist policymakers and service providers to better understand the complex and nuanced lived experiences of racialized disabled job seekers and workers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".