The influence of reasons for attending university on university experience: A comparison between students with and without disabilities
Bibliographic record
Abstract
Students choose to go to university for many reasons. They include those with disabilities and those without. The reasons why students with disabilities go to university and how these reasons impact university experience, including coping (academic resourcefulness), adapting, academic ability beliefs (academic self-efficacy), and grades, are investigated. Results show that unlike non-disabled peers, first-year students with disabilities who go to university for internal reasons (e.g. for the challenge, because they like learning) show higher academic resourcefulness and self-efficacy, and that those disabled students who choose to go to university in order to get a better job show higher academic self-efficacy. Upper-year students with disabilities less often choose to go to university for others and in order to get a better job than counterparts without disabilities. Upper-year students with disabilities less often choose to go to university for the university features (e.g. student services) than first-year students with disabilities. Upper-year students with disabilities choosing to go to university in order to delay responsibilities are less adapted, and those choosing to go for the reason of getting a better job have lower grades. Recommendations on strategies to increase student coping and self-efficacy and the need for qualitative research are made.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".