Persistent Gaps and Promising Practices in Recruiting, Hiring, and Retaining Canadians with Speech Disabilities
Bibliographic record
Abstract
Canadians with speech disabilities continue to experience significant barriers to employment despite equity and accessibility legislation. This study examined the employment experiences of adults with speech disabilities, including those who use augmentative and alternative communication (AAC). Ten eligible Canadian adults who self-identified as having a speech disability completed a quantitative questionnaire followed by a qualitative interview. Participants communicated using a range of methods, from speech alone to intermittent or primary AAC. Interviews were conducted via Zoom or Slack, and transcripts were analyzed inductively using reflexive thematic analysis. Themes and subthemes were organized around the employment lifecycle. A cross-cutting theme of Communication Strategies shaped experiences at every stage. Three systemic barriers also spanned the lifecycle: Lack of Awareness, Knowledge, and Training; Attitudinal Barriers; and Legislation, Policy, and Human Rights. Participants demonstrated they have the education, experience, and skills to contribute meaningfully to workplaces. However, key results revealed widespread lack of awareness about speech disabilities among recruiters, employers, and employment services; inconsistencies between inclusive policies and practices; psychological distress during job searching and interviews; and the inequity of traditional interviews as a hiring tool. Workplace accommodations were inconsistently approved and often dependent on employer attitudes. Systemic barriers within legislation, policy, and human rights processes further limited equitable employment. A multi-level response is needed to improve employment outcomes. Suggested recommendations include increasing awareness and training about speech disabilities for recruiters, employers, and employment services; fostering inclusive workplace cultures; ensuring accommodations are consistently available; and incorporating speech disabilities into national data collection and employment research. Addressing persistent barriers and supporting promising practices are essential to advancing employment equity for Canadians with speech disabilities.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".