Determinants of Students with Disabilities’ Attainment of Graduate/Professional Program Admission Credentials
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".