Assistive technology use and its associated factors among university students with disabilities: a case study in a developing country-mixed study design
Bibliographic record
Abstract
PURPOSE: Despite the recognized benefits, access to assistive technology (AT) remains limited. Identifying the current usage patterns and unmet needs of AT users could help address the challenges of students with disabilities. Thus, this study aimed to investigate AT use and its associated factors. METHODS: An institution-based cross-sectional mixed study was conducted on higher education students with disabilities in Ethiopia. An interviewer-administered questionnaire and an in-depth interview technique were used to collect data. Descriptive statistics and binary logistic regression models were used to analyse the quantitative data, while inductive thematic analysis was undertaken for the qualitative data. RESULTS: A total of 233 (74.68%) with (95% CI: 70%-80%) students with disabilities used at least one form of AT. Four themes emerged which include experiences of AT use and disability, benefits and challenges of using AT, perception of the community towards AT, and responsibility for the provision of AT. Students with vision problems or those with severe disability types were most likely to be AT users. CONCLUSION AND RECOMMENDATIONS: A significant proportion of students with disabilities had unmet needs for AT. Capacity at universities or rehabilitation centres would enhance access, usage of AT, and the unmet needs of students with disabilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".