Pre-employment transition and vocational rehabilitation services: Experiences in response to Vermont’s work-based learning program
Bibliographic record
Abstract
BACKGROUND: State vocational rehabilitation (VR) agencies offer pre-employment transition services (pre-ETS) and other VR services to high school students, but the literature has not documented differences in pre-ETS use by individual characteristics or across individual services. OBJECTIVE: We describe variation in how high school students used services from the Vermont VR agency and how a demonstration program emphasizing work-based learning experiences affected that use. METHOD: The study uses a descriptive approach to explore patterns in youth’s pre-ETS and VR services and outcomes two years after enrolling in a demonstration program. It compares youth with access to demonstration services (the treatment group) to those using usual services (the control group). RESULTS: Among all control group youth, more than half only used pre-ETS during a 24-month period, while about one-quarter used VR services and the remainder used no services from the VR agency. In contrast, nearly all treatment group youth used some VR services, with a majority (59 percent) using both VR services and pre-ETS. Control group youth who used pre-ETS and VR services differed from those who did not use these services by gender, disability type, employment, and service receipt characteristics; treatment group youth had fewer such differences. Earnings outcomes did not vary in consistent or interpretable ways. CONCLUSION: The findings demonstrate how an intervention designed to promote work-based learning experiences increased pre-ETS and VR use and decreased subgroup differences in service utilization. VR administrators might consider collecting information on potentially eligible students to increase access to and use of services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".