Evaluating the Impact of Assistive Technologies on Individuals With Disabilities in Benin: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: A significant proportion of individuals with disabilities in resource-limited countries require at least 1 assistive technology (AT) device to enhance their functioning and autonomy. However, there is limited evidence regarding the actual needs of AT users in these regions concerning the adequacy of ATs. OBJECTIVE: This research aims to assess the effects of ATs on AT users in a resource-limited country. METHODS: A cross-sectional study will be conducted in Benin, a sub-Saharan African country, using a nonprobability sample of AT users. Participants will undergo evaluation using standardized tools to assess their psycho-affective status, satisfaction with ATs, perception of the functional effects of ATs, well-being, and quality of life. Additionally, a survey based on the World Health Organization's rATA (rapid assistive technology assessment) tool will be conducted to gather sociodemographic and other data concerning the use of ATs. The findings will be organized and discussed using the Consortium on Assistive Technology Outcomes Research taxonomy, focusing on aspects related to the effectiveness and social significance of ATs, as well as the subjective well-being of AT users. RESULTS: The process of identifying potential participants began in August 2024, and data collection is scheduled to start in January 2025 and continue for 12 months. CONCLUSIONS: This research will provide an overview of the effects induced by the use of ATs, as well as describe the profile of AT users in Benin. To our knowledge, this will be the first study to examine the impact of ATs in Benin. It will therefore make a significant contribution to the existing data on the use of ATs in sub-Saharan Africa. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60869.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".