The Importance of Building Soft Skills in Vocational Training for Youth with Intellectual and Developmental Disabilities: Evidence from the Impact Project
Bibliographic record
Abstract
Background Research has identified employment as a social inclusion priority for individuals with intellectual and developmental disabilities (IDD) and has emphasized person-centered vocational training as a predictor of future employment. The Impact Project is a summer program offered by project partners in British Columbia, Canada, that provides youth with IDD vocational training to improve their employment experiences in preparation for future employment. Objective This study explicates the importance of soft skills in vocational training identified by youth and their parents/carers regarding attained employment experiences during the Impact Project (2020–2022). Methods This study evaluates qualitative data from youth and their parents/carers who reflected on their vocational training and attained employment experiences. Results Qualitative findings highlight the significance of soft skills, namely confidence, social capital, and job readiness in the youth's employment experiences and outcomes. Youth and parent/carer observations about these soft skills add insight to understanding positive employment outcomes from the Impact Project. Conclusion Qualitative data from the Impact Project (2020–2022) illuminate how soft skills contributed to the youth's employment experiences. These findings contextualize quantitative employment outcomes and can guide vocational training programs for youth with IDD in preparation for future employment.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".