Assistive technology and daily living challenges among students with disabilities at University of Gondar, Ethiopia: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Disability influences activities of daily living, leading to unsafe conditions, poor quality of life, and dependence on others and assistive technologies. Despite limited access and unmet needs, assistive technology enables users to participate in education and be independent members of their community. Students with disabilities in higher education face many challenges in their day-to-day activities and evidence is limited in the study area. Therefore, this study aimed to explore assistive technology experience and daily living challenges among students with disabilities in higher education. METHOD: A descriptive qualitative study design was employed at the University of Gondar, Gondar, Ethiopia, between December 20, 2022, and January 20, 2023. A purposive sampling method was employed to recruit 14 students with disabilities. An in-depth interview was employed using semi-structured questionnaires. Open Code version 4 software for coding and reflexive thematic analysis approach was employed for the analysis. RESULT: A total of 14 students with disabilities were included in an in-depth interview. Four main themes emerged, which included activities of daily living, attitudes toward people with disabilities, barriers to accessibility, and access to assistive technology. CONCLUSION: Barriers to activities of daily living among students with disabilities were poor accessibility of infrastructural facilities, lack of teaching/learning materials in an accessible format, and negative attitudes. The present study's finding is needed to support students in higher education for their academic achievement and to design appropriate rehabilitation strategies and policies on the accessibility of physical infrastructures, inclusive education, and the provision of assistive technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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; a candidate call from one teacher head, 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".