Self-reported assistive technology outcomes and personal characteristics in college students with less-apparent disabilities
Bibliographic record
Abstract
The impact of assistive technology (AT) services for college students with less-apparent disabilities is under-reported. Using the Canadian Occupational Performance Measure (COPM), we assessed student Performance and Satisfaction ratings of common academic tasks at the start and end of a semester during which 105 student-clients with less-apparent disabilities received AT services. We examined if COPM scores related to personal characteristics of gender, class-level (e.g., Sophomore), and STEM education; if personal characteristics predicted a student's follow-through with an AT service referral (n=231); and if personal characteristics and initial COPM scores predicted dropout from AT services (n=187). COPM ratings significantly increased in all academic tasks (p<.001). Gender predicted initial Satisfaction (male ratings > female ratings; p=.01), and Performance changes (females were more likely to have a service-meaningful change; p=.02). Higher class-level predicted better follow-through with a referral for AT services (p=.006). Increasing class-level (p=.05) and higher initial studying (p<.006) and reading (p<.029) ratings predicted a lower likelihood for dropout. These findings demonstrate that college students with less-apparent disabilities experience substantial improvements in their self-ratings of academic performance and satisfaction following AT services. Gender, class-level, and initial self-perceived reading and studying abilities may influence if and how the student participates with AT services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".