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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".