Meta‐Analysis of Soft Skills Interventions for Transition‐Age Autistic Individuals
Bibliographic record
Abstract
There has been growing interest in developing and evaluating soft skills interventions for transition-age autistic individuals. While many interventions demonstrated effectiveness in improving social competence, there is limited evidence on the pooled effectiveness of these interventions. In response to the research gap, this study aimed to investigate the effectiveness of soft skills interventions in enhancing social competence among transition-age autistic individuals. A total of 18 articles consisting of eight randomized controlled trials and 10 pre- and post-intervention studies were identified after a systematic review, and the effectiveness of these interventions was examined using the meta package on R 4.4.1. The analysis revealed overall positive effects of soft skills interventions in social adjustment (g = 0.53, p < 0.0001), social performance (g = 0.87, p < 0.001), and social skills (g = 0.53, p < 0.0001) among the autistic individuals. Moderation analyses indicated no significant impact of sample and intervention characteristics on soft skills outcomes. This meta-analysis highlights the importance of soft skills interventions for transition-age autistic individuals in preparing for successful careers.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".