Experiences of Autistic Students in Postsecondary Education: A Review of Reviews
Bibliographic record
Abstract
Background: The number of autistic students enrolling in postsecondary education is rising, accompanied by an increase in research and subsequent reviews (i.e., meta-analyses, systematic and scoping reviews) describing the experiences of autistic postsecondary students. We summarize the current state of the literature by describing the characteristics (e.g., publication year, language use), evaluating the quality, and mapping the domains and findings of reviews examining autistic postsecondary students to inform future directions of this research. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews and meta-analyses were followed. Reviews were included if they were (1) published between January 2000 and December 2023; (2) focused on postsecondary students who had a diagnosis of autism or self-identify as autistic; and (3) focused on experiences of autistic students in postsecondary settings. Consistent with other reviews of reviews, articles were coded for quality, including publication bias. Thematic analysis was used to extract themes from reviews. Results: = 20) was mostly acceptable; however, only three assessed publication bias and eight appraised study quality. Six themes were constructed from coded information identifying gaps, main findings, and review strengths as follows: (1) the need for methodological rigor; (2) the need for evidence-based, individualized supports; (3) the need to consider autistic students as a heterogenous population with diverse academic experiences; (4) the need to understand nonacademic factors impacting academic experiences; (5) research addressing or highlighting relevant gaps; and (6) research guided by lived experience and frameworks. Conclusions: We summarize key findings from the current literature and make relevant recommendations to move the research on autism in postsecondary forward.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".