Exploring employment readiness through mock job interview and workplace role-play exercises: comparing youth with physical disabilities to their typically developing peers
Bibliographic record
Abstract
PURPOSE: To assess performance differences in a mock job interview and workplace role-play exercise for youth with disabilities compared to their typically developing peers. METHODS: We evaluated a purposive sample of 31 youth (15 with a physical disability and 16 typically developing) on their performance (content and delivery) in employment readiness role-play exercises. RESULTS: Our findings show significant differences between youth with disabilities compared to typically developing peers in several areas of the mock interview content (i.e. responses to the questions: "tell me about yourself", "how would you provide feedback to someone not doing their share" and a problem-solving scenario question) and delivery (i.e. voice clarity and mean latency). We found no significant differences in the workplace role-play performances of youth with and without disabilities. CONCLUSIONS: Youth with physical disabilities performed poorer in some areas of a job interview compared to their typically developing peers. They could benefit from further targeted employment readiness training. IMPLICATIONS FOR REHABILITATION: Clinicians should: Coach youth with physical disability on how to "sell" their abilities to potential employers and encourage youth to get involved in volunteer activities and employment readiness training programs. Consider using mock job interviews and other employment role-play exercises as assessment and training tools for youth with physical disabilities. Involve speech pathologists in the development of employment readiness programs that address voice clarity as a potential delivery issue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".