Online Self-Determination Toolkit for Youth With Disabilities: Protocol for a Mixed Methods Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Youth with disabilities encounter many challenges during their transition to adulthood including finding employment. Jobs are often inaccessible, and youth often face a lack of support, discriminatory attitudes, and sometimes low self-confidence. Therefore, it is critical to help youth enhance their self-determination skills to advocate for their needs in the workplace. OBJECTIVE: The aim of this paper is to describe how an online toolkit aimed to improve self-determination in advocating for needs, including disability disclosure and accommodation requests to employers, was co-created with youth with disabilities. METHODS: We will use a mixed method design in which qualitative data (ie, focus groups and mentored discussion forum) are collected to understand the contextual factors during the intervention that could affect outcomes or explain results through the pre-post questionnaires. Fifty youths with disabilities aged 15 to 24 years will be recruited. RESULTS: Data collection is in progress. Planned analyses include focus groups and pre-post surveys to determine the impact of the intervention on self-determination. A qualitative content analysis of the focus groups and all open-ended survey questions will be conducted to understand the impact of the toolkit. CONCLUSIONS: Our online toolkit includes evidence-informed content that was co-created with youth who have a disability. It has potential for educational and vocational programming for youth with disabilities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/20463.
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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 | 0.103 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".