ABSTRACT
BACKGROUND: Verbatim transcription of qualitative audio data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis, and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or "per-minute" fees, data protection concerns, and limited availability of such programs in many languages. In this article, we outline our process of adapting a free, open-source, speech-to-text algorithm (Whisper by OpenAI) into a usable and accessible tool for qualitative transcription. Our program, which we have dubbed "Vink" for voice to ink, is available under a permissive open-source license (and thus free of cost). RESULTS: We conducted a proof-of-principle assessment of Vink's performance in transcribing authentic interview audio data in 14 languages. A majority of pilot-testers evaluated the software performance positively and indicated that they were likely to use the tool in their future research. Our usability assessment indicates that Vink is easy-to-use, and we performed further refinements based on pilot-tester feedback to increase user-friendliness. CONCLUSION: With Vink, we hope to contribute to facilitating rigorous qualitative research processes globally by reducing time and costs associated with transcription and by expanding free-of-cost transcription software availability to more languages. With Vink running on standalone computers, data privacy issues arising within many other solutions do not apply.
Subject(s)
Ink , User-Computer Interface , Speech , SoftwareABSTRACT
Self-testing is an effective tool to bridge the testing gap for several infectious diseases; however, its performance in detecting SARS-CoV-2 using antigen-detection rapid diagnostic tests (Ag-RDTs) has not been systematically reviewed. This study aimed to inform WHO guidelines by evaluating the accuracy of COVID-19 self-testing and self-sampling coupled with professional Ag-RDT conduct and interpretation. Articles on this topic were searched until November 7th, 2022. Concordance between self-testing/self-sampling and fully professional-use Ag-RDTs was assessed using Cohen's kappa. Bivariate meta-analysis yielded pooled performance estimates. Quality and certainty of evidence were evaluated using QUADAS-2 and GRADE tools. Among 43 studies included, twelve reported on self-testing, and 31 assessed self-sampling only. Around 49.6% showed low risk of bias. Overall concordance with professional-use Ag-RDTs was high (kappa 0.91 [95% confidence interval (CI) 0.88-0.94]). Comparing self-testing/self-sampling to molecular testing, the pooled sensitivity and specificity were 70.5% (95% CI 64.3-76.0) and 99.4% (95% CI 99.1-99.6), respectively. Higher sensitivity (i.e., 93.6% [95% CI 90.4-96.8] for Ct < 25) was estimated in subgroups with higher viral loads using Ct values as a proxy. Despite high heterogeneity among studies, COVID-19 self-testing/self-sampling exhibits high concordance with professional-use Ag-RDTs. This suggests that self-testing/self-sampling can be offered as part of COVID-19 testing strategies.Trial registration: PROSPERO: CRD42021250706.
