Lessons from the pilot of a mobile application to map assistive technology suppliers in Africa
Bibliographic record
Abstract
A pilot project to develop and implement a mobile smartphone application (App) that tracks and maps assistive technology (AT) availability in southern Africa was launched in Botswana in 2016. The App was developed and tested through an iterative process. The concept of the App (AT-Info-Map) was well received by most stakeholders within the pilot country, and broader networks. Several technical and logistical obstacles were encountered. These included high data costs; difficulty in accessing AT information from the public healthcare sector, the largest supplier of AT; and the high human resource demand of collecting and keeping up-to-date device-level information within a complex and fragmented supply sector that spans private, public and civil society entities. The challenges were dealt with by keeping the data burden low and eliminating product-level tracking. The App design was expanded to include disability services, contextually specific AT categories and make navigation more intuitive. Long-term sustainability strategies like generating funding through advertisements on the App or supplier usage fees must be explored. Outreach and sensitisation programmes about both the App and AT in general must be intensified. The project team must continually strengthen partnerships with private and public stakeholders to ensure ongoing project engagement. The lessons learnt might be of value to others who wish to embark on initiatives in AT and/or implement Apps in health or disability in southern Africa and in low-resourced settings around the world.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".