A systematic review of mentorship programs to facilitate transition to post-secondary education and employment for youth and young adults with disabilities
Bibliographic record
Abstract
PURPOSE: Youth with disabilities experience barriers in transitioning to Post-Secondary Education (PSE) and employment. Mentorship programs provide a promising approach to supporting youth through those transitions. This paper aims to identify the effective components of mentorship programs and describe participants' experiences. METHOD: We undertook a systematic review of mentorship interventions for youth and young adults with disabilities. We searched seven electronic databases for peer-reviewed articles published in English between 1980 and 2014. We included articles that examined mentorship interventions focused on PSE or employment outcomes among youth, aged thirty or younger, with physical, developmental, or cognitive disabilities. RESULTS: Of the 5068 articles identified, 22 met the inclusion criteria. For seven mentorship interventions, at least one significant improvement was reported in school- or work-related outcomes. Mentorship programs with significant outcomes were often structured, delivered in group-based or mixed formats, and longer in duration (>6 months). Mentors acted as role models, offered advice, and provided mentees with social and emotional support. CONCLUSIONS: Evidence suggests that mentorship programs may be effective for helping youth with disabilities transition to PSE or employment. More rigorously designed studies are needed to document the impact of mentorship programs on school and vocational outcomes for youth with disabilities. Implications for Rehabilitation Mentorship interventions have the potential to effectively support youth with disabilities as they transition to post-secondary education and employment. Youth should consider participating in formal mentorship interventions, and clinicians and educators should encourage them to do so, to enhance social, educational, and vocational outcomes. When developing interventions, clinicians should consider incorporating the effective components (i.e. duration, content, format) of mentorship interventions identified in this paper. Future mentorship programs should also contain a rigorous evaluation component. Clinicians can help to create (build content, consult on accessibility), connect (youth to program, program to community agencies), and contribute to mentorship interventions.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".