Autor: |
P. Bondaronek, T. Papakonstantinou, C. Stefanidou, T. Chadborn |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
|
Zdroj: |
Public Health in Practice, Vol 6, Iss , Pp 100401- (2023) |
Druh dokumentu: |
article |
ISSN: |
2666-5352 |
DOI: |
10.1016/j.puhip.2023.100401 |
Popis: |
Objectives: The UK government's approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing Services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable. The specific objectives were to 1) evaluate a human-in-the-loop machine learning technique based on structural topic modelling in terms of its Service ability in the analysis of vast volumes of free-text data, 2) generate actionable themes that can be used to increase user satisfaction of the Service. Methods: We evaluated an unsupervised Topic Modelling approach, testing models with 5–40 topics and differing covariates. Two human coders conducted thematic analysis to interpret the topics. We identified a Structural Topic Model with 25 topics and metadata as covariates as the most appropriate for acquiring insights. Results: Results from analysis of feedback by 37,914 users from May 2020 to March 2021 highlighted issues with the Service falling within three major themes: multiple contacts and incompatible contact method and incompatible contact method, confusion around isolation dates and tracing delays, complex and rigid system. Conclusions: Structural Topic Modelling coupled with thematic analysis was found to be an effective technique to rapidly acquire user insights. Topic modelling can be a quick and cost-effective method to provide high quality, actionable insights from free-text feedback to optimize public health Services. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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