A review of employment outcome measures in vocational research involving adults with neurodevelopmental disabilities
Bibliographic record
Abstract
BACKGROUND: Adults with neurodevelopmental disability (NDD) have poor employment outcomes when compared to their peers without disabilities. Examining employment outcomes beyond common dichotomous descriptive metrics (i.e., employed versus unemployed) depends on the use of standard measures and structured procedures. OBJECTIVE: This review of vocational research literature focused on identifying measures of employment outcomes for adults with NDD. METHODS: Searches were conducted across five databases - ERIC, MEDLINE, CINAHL, HaPI, and PsycINFO. Screening was conducted in duplicate, with all disagreements adjudicated by the senior researcher. RESULTS: A total of 45 articles met inclusion criteria, and data extraction revealed that 64 different employment measures were used in these vocational research studies. CONCLUSIONS: This work summarizes the employment measures for people with NDD utilized in the literature. Descriptions of these measures were provided and coding by person and environment themes, which is a useful resource for planning future vocational research for people with NDD.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".