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Using Visual Timelines in Telephone Interviews: Reflections and Lessons Learned From the Star Family Study.

ABSTRACT: Visual timeline methods have been used as part of face-to-face qualitative interviewing with vulnerable populations to uncover the intricacies of lived experiences, but little is known about whether visual timelines can be effectively used in telephone interviews. In this article, we reflect on the process of using visual timelines in 16 telephone interviews with women as part of the "STarting a family when you have an Autoimmune Rheumatic disease" study (STAR Family Study). The visual timeline method was used to empower women to organize and share their narratives about the sensitive and complex topic of starting a family. We conducted a thematic analysis of the audio-recorded interview data, using researchers' field notes and reflections to provide context for our understanding of the benefits of using timelines and to understand the process of using visual timelines during telephone interviews. Resource packs were sent to women before study participation; 11 of the 16 women completed a version of the timeline activity. Six themes were identified in the methodological data analysis: (1) use and adaptation of the timeline tool, (2) timeline exchange, (3) framing the interview: emphasizing that women are in control, (4) jumping straight in, (5) taking a lead, and (6) disclosing personal and sensitive experiences. The use of visual timelines facilitated interviewee control and elicited rich narratives of participants' experiences in telephone interviews. Women created their visual timelines autonomously and retained ownership of their timeline data; these features of the data generation process need to be considered when using visual timelines in telephone rather than face-to-face interviews. Use of visual methods within telephone interviews is feasible, can generate rich data, and should be further explored in a wider range of settings.

SUBMITTER: Pell B

PROVIDER: S-EPMC7901046 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:There is a recognised need for innovative methods to elicit the perspective of adolescents on public health issues, particularly when addressing sensitive topics such as the impact of mining projects on their health. Participatory approaches such as "photovoice" allow for deep engagement of vulnerable and marginalised populations, including adolescents. However, few existing studies have used the photovoice method to reflect on issues related to the environment and its impact on public health. To date, no studies have been found that have used photovoice to gain insight into adolescents' perspectives in mining areas. In this paper, we discuss the application of the photovoice method to understand adolescents' perceptions about the impact of mining on their health and well-being in rural areas in Mozambique. The study was conducted in northern and central Mozambique. Photovoice was successfully integrated into eight focus group discussions with adolescent girls and boys aged 15 to 17 years. Several lessons for guiding future research were learned. First, it provided an understanding of the perceived impacts of mining on their health and well-being. Second, photovoice promoted active engagement and interest in the study by the adolescents. Finally, compared to its ability to capture perceptions of physical and environmental aspects affecting adolescents' well-being, the method was less straightforward in revealing their concerns regarding social, relational and community aspects that are less tangible. Programs can make use of photovoice to address health issues without setting adolescents' views and priorities aside, allowing them to influence health decisions on issues that are meaningful to them. Future studies should explore strategies to minimise the role of the power dynamics that affect the engagement and contribution of adolescents in advocating for necessary and meaningful changes. Additionally, it is important to investigate how health programs and policies can help to reduce the impact of existing inequalities.

Project description:BackgroundDeep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.Methods and findingsAll adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient's prediction.ConclusionsWe converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.

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Using Visual Timelines in Telephone Interviews: Reflections and Lessons Learned From the Star Family Study.

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