Danemayer, Jamie;
Oldfrey, Ben;
Shrestha, Pratisthit Lal;
Holloway, Cathy;
(2023)
Harmonising Assistive Technology Assessment Data: A Case Study in Nepal.
Studies in Health Technology and Informatics
, 306
pp. 289-296.
10.3233/SHTI230633.
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Abstract
There is a practical demand to maximise existing data to understand and meet the assistive technology (AT) needs in dynamic populations. Harmonisation can generate new insight by integrating multiple datasets that were not previously comparable into a single longitudinal dataset. We harmonised AT assessment data from three population-based surveys collected several years apart in Nepal: the Living Conditions of Persons with Disabilities (2014-2015), the Multiple Indicator Cluster Survey (2019), and the rapid Assistive Technology Assessment (2022). The harmonised dataset demonstrates a method that can be used for unifying AT surveys in other settings and conducting trend analyses that are necessary for monitoring a population's dynamic AT needs. We set out to explore AT data's potential for harmonisation, and learned there is indeed value in this approach for situating disparate datasets, though the methodology proposed will need further validation.
| Type: | Article |
|---|---|
| Title: | Harmonising Assistive Technology Assessment Data: A Case Study in Nepal |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/SHTI230633 |
| Publisher version: | https://doi.org/10.3233/SHTI230633 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Harmonisation, Statistical matching, Survey Data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10177805 |
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