Disability in Graduate Education: Examining the Role of Resource Management and the Psychosocial Aspects of Student Experiences
Bibliographic record
Abstract
A significant portion of graduate students, approximately 30-50%, either do not complete their programs or receive the designation of All-But-Dissertation. To prevent the attrition of capable individuals, it is important to examine the experience of students in the distinct learning environment of graduate education. This study examined the psychosocial and learning experiences of graduate students with a disability. A sequential explanatory multiple-method research design examined the breadth and depth of graduate student experiences. In phase one, graduate students with and without disabilities (N = 302) at six universities in Ontario completed an electronic survey. The results of the survey data analysis identified disability status as an important factor in the psychosocial and resource management experiences of graduate students. In phase two, semi-structured individual interviews were conducted with 13 graduate students who identified as having a disability. The results of the interviews illuminated the impact of disability on the experience of students in graduate education. Themes related to the environment of graduate education, psychological and emotional well-being, important social relationships, disability status, and the resource management aspect of self-regulated learning where present in both phases of this study. The findings of this study have implications for research on graduate education, individuals enrolled in graduate studies, graduate supervisors, and university administration.
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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 | 0.003 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".