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Record W4411993397 · doi:10.15353/cjds.v13i3.1172

Barriers and Facilitators to Access to Post-secondary Education for Students with Learning Disabilities: A Narrative Literature Review

2024· article· en· W4411993397 on OpenAlexaffvenue
Cameron McKenzie, Sarah Southey

Bibliographic record

VenueCanadian Journal of Disability Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsNarrativePsychologyPedagogyNarrative inquiryMedical educationLearning disabilitySociologyMathematics educationMedicineDevelopmental psychologyLinguistics

Abstract

fetched live from OpenAlex

Our narrative literature review sought to identify potential barriers and facilitators to access to academic participation for post-secondary students with learning disabilities. A search of eight databases yielded over 1600 possible articles, reduced to 107 after applying selection criteria. We identified three themes: accommodations, self-advocacy, and supports. We found that current efforts are focused primarily on the provision of individualized accommodations to help students adapt to an inaccessible academic environment, despite a scarcity of evidence confirming their efficacy. Students must submit a psychoeducational assessment report to be eligible for accommodations, even though there is no clear relationship between the information they contain and the specific accommodations approved. The likelihood that students receive accommodations further depends on personal factors, including self-advocacy. Skills-based supports, mental health and wellbeing supports, and inclusive pedagogical methods act as facilitators to equity. Persistent barriers associated with retroactive accommodations could be substantially reduced if more resources were directed to proactive efforts to increase academic accessibility of post-secondary education.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.007
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.423
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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