High-Incidence Learning-Related Disabilities, Gender, and Educational and Employment Outcomes in Young Adulthood
Bibliographic record
Abstract
The transition into post-secondary education or employment presents significant challenges for youth with high-incidence disabilities affecting learning, most commonly learning disabilities and attention-deficit/hyperactivity disorder. To date, few longitudinal studies investigate this transition in youth with learning-related disorders specifically, especially while considering education and employment outcomes simultaneously. This study examined relationships between learning-related disabilities requiring an individual intervention plan (Individualized Education Program [IEP]) in high school and key transition outcomes in early twenties in Quebec ( N = 513; 61.4% with an IEP; 51.0% male). Compared with their normative peers, youth with learning-related disabilities were less likely to graduate from high school and enroll in college; more likely to be neither in education, employment, or training (NEET); and equally likely to be employed, regardless of the job type (career-related or not). Young women with disabilities were particularly likely to be NEET, and the gender gap in college enrollment favoring women narrowed among those with disabilities. Gender and disability status appear to intersect to shape critical early adulthood outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".