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Record W4402478941 · doi:10.14738/assrj.116.17135

Determinants of Students with Disabilities’ Attainment of Graduate/Professional Program Admission Credentials

2024· article· en· W4402478941 on OpenAlexaff
Joyce Ann Miguel, Kayla D. Bazzana-Adams, Michael deBraga, Stuart B. Kamenetsky

Bibliographic record

VenueAdvances in Social Sciences Research Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsPsychologyMedical educationMathematics educationPedagogyMedicine

Abstract

fetched live from OpenAlex

Multiple explanations have been provided as to why individuals with disabilities are so underrepresented in the ‘helping’ professions (e.g., medicine, social work, psychology and more). One possible reason is that as undergraduate students they were less likely to obtain the competitive credentials needed to gain admission to professional programs in such areas, either due to lower ability or lack of sufficient accommodations. The present study assessed the degree to which type and severity of disability, demographic factors (e.g., SES), dispositional factors (e.g., self-esteem), perceived stress, and perceived barriers are predictive of undergraduate students’ ability to obtain such credentials. A survey of 132 North American students suggests that dispositional factors such as the Big Five personality trails of Conscientiousness, Openness and lower Agreeableness, self-esteem and, self-efficacy/self-advocacy, as well as lower disability severity and higher SES are the strongest predictors of success in obtaining competitive post-graduate admission credentials. We propose that, similar to other demographic groups, dispositional factors are the strongest predictors of resilience necessary for academic success.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.225
GPT teacher head0.611
Teacher spread0.386 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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