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Can you repeat the question? Paradata as a lens to understand respondent experience answering cognitively demanding, sensitive questions.

ABSTRACT: Survey researchers hope that respondents will provide high-quality data, but evidence suggests that social desirability bias may be commonplace. Social desirability can lead to significant underreporting or overreporting of sensitive behaviors. With better understanding of the cognitive processes that respondents use to prepare and deliver their responses, survey designers could hope to minimize social desirability bias or at least detect settings that lessen its impact. The primary objective of this study was to use survey paradata to understand the psychology of responding to certain types of survey questions. More specifically, we sought to determine how emotional triggering can alter response latencies to cognitively demanding and sensitive survey questions on induced abortion, which is underreported. We hypothesize that having had a prior abortion might lengthen response times to an indirect question about abortion among respondents who have experienced this sensitive reproductive outcome as they hesitate in deciding whether and how to respond to the question. Data come from a representative survey of 6,035 reproductive age women in Rajasthan, India. We used list experiment question active screen time paradata in conjunction with responses from direct questions on abortion to assess our hypothesis. Our final model was a multivariate linear regression with random effects at the level of the interviewer, including adjustments for respondent, community, and interviewer characteristics to estimate within-respondent effects. Results suggest that women who reported an abortion on the direct abortion questions took 5.11 (95% CI 0.21, 10.00) seconds longer to respond to the list experiment treatment list compared to the control list in comparison to women who did not report an abortion on the direct abortion questions. This study demonstrates the additional insights gained when focusing on response latencies to cognitively demanding questions involved in the measurement of sensitive behaviors.

SUBMITTER: Bell SO

PROVIDER: S-EPMC8183984 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.ResultsWe extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%).ConclusionsIn this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

Project description:BackgroundCOVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive.ObjectiveA potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources.MethodsGiven a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions.ResultsWe applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19-related questions.ConclusionsCompared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19-related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

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Can you repeat the question? Paradata as a lens to understand respondent experience answering cognitively demanding, sensitive questions.

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