Identifying barriers in access to postsecondary education among students with disabilities
Bibliographic record
Abstract
Despite many countries being signatories to human rights treaties, such as the United Nations Convention on the Rights of Persons with Disabilities, which emphasises equity, diversity, inclusion, and access for all individuals, there remains an underrepresentation of students with disabilities in higher education. This Master of Research aims to identify barriers in access to postsecondary education among students with disabilities. To achieve this, I undertook a systematic literature review of evidence in the field. Forty-six studies were included in the review. The majority of the studies (33) were carried out in the United States, followed by the United Kingdom (5) and Ireland (3). Single studies were conducted in Canada, Belgium, Sweden, Australia, and Spain. Of the total number of studies analysed, it was found that 37 utilised a qualitative design, followed by seven that used a mixed-method approach and two studies employed a quantitative methodology. Thematic analysis revealed seven recurring themes of barriers encountered by students with disabilities during their transition from secondary to postsecondary education. These themes comprised Personal and Psychological Barriers, Family Influence and Background, Financial Challenges, Educational and Institutional Barriers, Social Stigma and Discrimination, Institutional and Policy Barriers, and Accessibility and Accommodation Challenges. The findings have significant implications for both policy and practice, particularly for education policy. Additionally, it contributes to wider discussions on how to effectively empower and assist students with disabilities in their academic pursuits.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".