Supporting Autistic Adults in Postsecondary Settings: A Systematic Review of Peer Mentorship Programs
Bibliographic record
Abstract
Background: The number of autistic individuals attending college or university is increasing, yet graduation rates are low as postsecondary environments often fail to support autistic students' individual needs. Peer mentorship programs are emerging as a promising approach for providing individualized, one-on-one support to meet this service gap for autistic postsecondary students. However, no literature has systematically described these programs. Methods: We conducted a systematic review that described existing peer mentorship programs for autistic students in postsecondary education as well as their effectiveness. Results: Our search of five databases found nine unique programs that were evaluated in 11 peer-reviewed articles. Programs reported positive outcomes in various domains, which included social skills, academic performance, and sense of belonging. The evidence for these programs was primarily qualitative, sample sizes were small, and there was considerable heterogeneity in the format, provision, and goals of these programs, as well as the evaluation methods used. Conclusions: Overall, the state of the research related to the efficacy of peer mentorship programs for autistic students remains in its infancy, and further research is needed to quantify effectiveness and enable program comparisons. Lay summary: This article provides a summary of the kinds of supports available to autistic adults within postsecondary settings, which may help autistic adults explore options for their own education. Advancing research in this area may improve the college/university experience for autistic adults in the future.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".