Young Adults With Developmental Language Disorder: A Systematic Review of Education, Employment, and Independent Living Outcomes
Bibliographic record
Abstract
Purpose Research on developmental language disorder (DLD) in adulthood has increased rapidly in recent years. However, to date, there has been no systematic literature review on this topic, thereby limiting the possibility to have a comprehensive overview of publications in this field. Method Following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we conducted a systematic literature review. A literature search was undertaken in four databases, from 2005 to 2018. We selected articles with original data related to life outcomes of young adults with and without DLD, all aged between 18 and 34 years, in three life areas: education, employment, and independent living. Methodological characteristics of the studies were analyzed. Results Fifteen articles were selected with longitudinal designs. In every life area, young adults with DLD were compared to their typically developing peers to identify their strengths and weaknesses. The predictive role of language abilities was also examined. Conclusions Outcomes within each life area are heterogeneous. Nevertheless, similarly to young children and adolescents, young adults with DLD face numerous challenges. Although language abilities partly predict some of these outcomes, much of the variance remains unaccounted for and some outcomes are unrelated to this predictor. This systematic literature review has implications for researchers and practitioners to identify promising avenues for research, interventions, and policy development. Supplemental Material https://doi.org/10.23641/asha.13022552.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".