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Record W4409640234 · doi:10.22329/jcrid.v1i1.8400

Equal Access? Comparing Accommodation and Treatment Experiences of Racialized University Students with Attention Problems and Their White Peers

2024· article· en· W4409640234 on OpenAlexaffabout
Sanya Sagar, John Freer, Tisha J. Ornstein, Carlin J. Miller

Bibliographic record

VenueJOURNAL OF CRITICAL RACE INDIGENEITY AND DECOLONIZATION · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAccommodationWhite (mutation)PsychologyRace (biology)Social psychologyMathematics educationSociologyGender studies

Abstract

fetched live from OpenAlex

University students who have difficulty with attention problems, including but not limited to those with ADHD, may struggle to focus, organize, and manage their education independently at the postsecondary level. This study examined the intersectional experience of racialized students with attention problems and their access to academic support services at two Canadian universities. Racialized participants (n=198) were compared with White participants (n=120). The goal was to investigate the relations between ADHD symptoms/probable diagnoses, academic difficulties, and access to medication and accommodation supports. Although the results revealed a similar occurrence of ADHD symptoms, previous diagnoses of ADHD, and academic difficulties across the groups, treatment and accommodation were not equal. Specifically, racialized students reported fewer prescriptions for stimulants and fewer academic accommodations through the universities’ disability services offices. Overall, these findings suggest that although there are not substantial differences in need, racialized students are not receiving the same level of support as their White peers. Further research is recommended as well as changes to practice guidelines such that more support and better access to services for individuals from historically underserved groups will be provided.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.359
Teacher spread0.320 · 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 teacher head, not a consensus.

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

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

Citations2
Published2024
Admission routes2
Has abstractyes

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