Go-Along Virtual INTERview (GVIv): A Novel Nurse-Led Qualitative Method for Exploring Neighborhood Lived Experience and Health

Abstract

Health and quality of life vary across neighborhoods, showing that where people live shapes how they live. However, current quantitative geo-focused methods exploring neighborhood impacts on health do not fully capture how people experience places. Nurse researchers’ grounding in the nursing metaparadigm (person, environment, health, and nursing) positions them to lead inquiry into how social and physical neighborhood environments affect health. We propose a nursing-led Go-along Virtual INTERview (GVIv) approach that utilizes digital street-view platforms to conduct virtual interviews to investigate how neighborhoods shape lived experiences. Guided by the Political Ecology Framework to contextualize historical, cultural, and political-economic influences on health, and principles of community-engaged research, we used the GVIv approach to situate narratives of individuals with opioid use disorders in Chicago. This approach combines in-person and virtual semi-structured interviews to explore how built, social, economic, cultural, and temporal neighborhood factors shape health. Our pilot application of GVIv showed promise in eliciting rich contextual data, shifting researcher–participant dynamics, reducing resource expenditure, and enhancing safety. GVIv expands nursing research by offering a participant-centered, scalable approach to examine place-based health influences and inform equitable policy and intervention design.

Introduction and Background

Neighborhoods have a powerful influence on health; they do not just describe where people live but also influence how they live. Life expectancy and health outcomes vary widely across neighborhoods globally (Hosokawa et al., 2024; Moser et al., 2014). Within the United States (U.S.), an extensive cross-sectional study representing approximately 90% of the census tracts found neighborhood conditions and resources explained approximately 50% of the variation in life expectancy at birth (Shanahan et al., 2022). The city of Chicago has a 15.1-year difference in life expectancy between neighborhoods that are approximately five miles apart (Hunt et al., 2015), and the difference in average life expectancy between non-Hispanic Black and non-Hispanic White populations was 8.21 years for females and 10.53 years for males (Bishop-Royse et al., 2024).

Reviews have found that neighborhood-level factors are associated with multiple health outcomes, such as depression (Sui et al., 2022), pediatric weight outcomes (Daniels et al., 2021), drug use behaviors related to HIV vulnerability (Brawner et al., 2022), and dementia (Zhao et al., 2021). Neighborhoods bring together various factors that shape people’s health and well-being by combining built and social environments (Diez Roux & Mair, 2010). Features of the built environment, including housing, parks, walkable streets, vacant lots, and green spaces, establish the physical landscape through which neighborhood social dynamics, such as cohesion and cultural norms, emerge and operate.

Although it is well recognized that where people live impacts their health and longevity (Ansell, 2017; Duncan & Kawachi, 2018), the vast number of neighborhood factors, heterogeneous operational neighborhood definitions, synergistic effects, and temporal changes within the same neighborhood can create challenges for researchers exploring the impact of neighborhoods on health (Diez Roux & Mair, 2010; Duncan et al., 2018). However, the growing availability of spatial data and advances in quantitative approaches, such as mathematical modeling (Stier et al., 2021), geographical information systems (Park et al., 2021), spatial epidemiology (Gebreab, 2018), and coincidence analysis (Rich et al., 2022) can be leveraged to address neighborhood and health research challenges, providing opportunities to deepen our understanding of how place impacts health. Even with these advances, the dynamic and reciprocal relationships between people and their environments are complex and cannot be fully captured using sophisticated quantitative methods that center on the quantification of structural factors only.

To holistically understand how built and social conditions influence health, researchers are increasingly adopting context-specific research approaches. Walking or go-along interviews (hereafter “go-alongs”) are a widely used, evidence-generating method for examining how neighborhoods and their residents shape and influence one another (Carpiano, 2009; Chang, 2017a; D’Errico & Hunt, 2022; Jackson et al., 2024; Odzakovic et al., 2020). Go-along interviews provide a method that allows for an in-depth exploration of how and where people live by integrating context into the interview. The interview embeds within the participant’s lived environment, enabling the researcher and participant to jointly discuss the neighborhood in real time, and create multi-layered place-based interviews that prompt meaning-making, observation, and memories. Thus, the neighborhood co-produces emic knowledge complementing etic knowledge from the participant (Brown & Durrheim, 2009). Despite these strengths, go-along interviews are resource- and time-intensive to implement and can be challenged by practical constraints, such as walkability, participant health status, neighborhood safety concerns, and weather conditions (Carpiano, 2009; Chang, 2017b; D’Errico & Hunt, 2022; Garcia et al., 2012).

Although neighborhood-level research predominantly falls within the public health discipline, nurse scientists have made enduring and growing contributions to this field (Brawner et al., 2022; Jackson et al., 2024; Milbrath & DeGuzman, 2015; Schroeder et al., 2022). Nursing’s long-standing engagement with communities, its focus on social determinants of health, and its holistic view of the person–environment relationship uniquely position nurses to lead research on how neighborhood context shapes health outcomes. Through geo-focused qualitative approaches, nurse researchers can provide textured insight into places, shedding light on how residents perceive and experience the interwoven built and social neighborhood in which they live. This perspective aligns with the nursing metaparadigm, which recognizes the environment as a core determinant of well-being, nurses’ holistic orientation to the person–environment relationship, and builds on the discipline’s tradition of using qualitative inquiry to surface issues of context, meaning, and sensemaking in everyday life. This approach can elicit an understanding of how neighborhoods influence health outcomes, combined with long-standing engagement in community health equity, therefore providing a critical perspective (and opportunity) for studying the environment as a core determinant of health and well-being.

Building on these strengths, we introduce a nursing-led methodological innovation, the Go-along Virtual INTERview (GVIv), as a qualitative method to explore how neighborhood conditions shape health. GVIv leverages traditional digital street view services (e.g., Google Street View, Apple Look Around, Microsoft Bing Streetside) to facilitate virtual go-along interviews aimed at visually and spatially exploring how neighborhood characteristics influence people’s lived experiences, emotions, and behaviors. In the following sections, we briefly review the current literature, explain the proposed GVIv approach and its implications for nursing science, and discuss the strengths, limitations, and potential mitigation strategies based on insights from our pilot implementation of this novel method.

Review of Literature

By 2010, researchers began using go-along interviews and street view services independently to examine health-related topics, developing a comprehensive understanding of their strengths and limitations. Table 1 summarizes the strengths and limitations of research using go-along interviews and street view services. Kusenbach (2003) first articulated the “go along” as an ethnographic tool to elicit the reflexive meanings individuals assign to place and activity. Carpiano (2009) later extended this work within public health, demonstrating how go-along interviews can illuminate relationships between neighborhood structures, social capital, and health. Since its introduction, versions of go-along interviews have been applied to a wide range of health-related topics, including dementia care (Odzakovic et al., 2020), substance use recovery (Chang, 2017a), responses to natural disasters (D’Errico & Hunt, 2022), nursing care (Jackson et al., 2024), and adolescents’ physical activity in urban spaces (Van Hecke et al., 2016).

Table 1.

Review of Literature: Strengths and Limitations.

Summary	Explanation	Key references
Go-along interviews
Strengths
Integration of context	Generates actionable and rich data on person and place interactions, spatial relationships, and embodied experiences that are otherwise invisible by integrating	Bartlett et al. (2023), Garcia et al. (2012), Jackson et al. (2024), Kusenbach (2003), Trell and Van Hoven (2010), Van Hecke et al. (2016)
Redistribution of power	At individual level, shifts participant and researcher dynamic, as participant assumes guiding role At societal level, challenges dominant ways of knowing by highlighting participants’ spatial perspective	Bartlett et al. (2023), Carpiano (2009), Chang (2017b), D’Errico and Hunt (2022) Bartlett et al. (2023), Castrodale (2018)
Facilitation of dialogue	Context can prompt participants to discuss experiences and becomes co-producer of knowledge	Brown and Durrheim (2009), D’Errico and Hunt (2022)
Limitations
Environment	Unpredictable weather or difficulty walking routes	Carpiano (2009), D’Errico and Hunt (2022), Garcia et al. (2012), Van Hecke et al. (2016)
Ambulation	Individuals with difficulty ambulating may opt out	Bartlett et al. (2023), Castrodale (2018), Chang (2017b), Garcia et al. (2012)
Physical or psychological safety	Concerns about being seen with unknown person in community. Some participants may not wish to return to old neighborhoods	Carpiano (2009), Castrodale (2018), Chang (2017b)
Data collection	Challenges integrating interview and environmental data	Carpiano (2009), Garcia et al. (2012)
Confidentiality	Interviews may inadvertently record passerby data and cameras may inadvertently capture people	Garcia et al. (2012), Jackson et al. (2024)
Street-view services
Strengths
Built environment audits	Follow-up surveys, geo-demographic data, or in-person audits validate built environment findings from street view services	Chiang et al. (2017), Clarke et al. (2010), Odgers et al. (2012)
Feasibility	Multiple applications, increased flexibility, eased study operations, decreased cost	Christman et al. (2020), Clarke et al. (2010), He and Li (2021), Kostakos et al. (2019), Odgers et al. (2012), Roberge et al. (2022), Rundle et al. (2011), Rzotkiewicz et al. (2018), Wu et al. (2014)
Use with machine learning	Machine learning techniques have utilized street view services and extracted built environment characteristics	He and Li (2021), Lu (2018), Najafi and Mohammadi (2024), Wang et al. (2019)
Temporal capacity	Allows visualization of same geographic area at different time points	Smith et al. (2021)
Limitations
Lack of nuance	Cannot examine details, social factors, subcultures, or activities within area	Bloch (2020), Chiang et al. (2017), Christman et al. (2020), Clarke et al. (2010), Najafi and Mohammadi (2024), Odgers et al. (2012)
Variability	Not all images are up-to-date or taken in the same year; some areas have more images and poor image quality	Clarke et al. (2010), Clews et al. (2016), He and Li (2021), Kostakos et al. (2019), Roberge et al. (2022), Rundle et al. (2011), Rzotkiewicz et al. (2018), Smith et al. (2021), Wu et al. (2014)
Potential bias	Due to the temporal differences and focus on visual features only, there is a risk of potential bias if data is used to make inferences	Bloch (2020), Smith et al. (2021)

Several technology companies provide online street view services, with Google Street View (GSV; Google Inc., Mountain View, CA, USA) being the most well-known model. This free digital platform allows visualization of locations worldwide with panoramic street-level views (Odgers et al., 2012). Launched in 2007, cameras on vehicles capture images to create this mobile mapping system (Charreire et al., 2014). Images are strung together and overlapped to create 360-degree views linked with GPS data (Rzotkiewicz et al., 2018). While older images are retained (Rzotkiewicz et al., 2018), Google continues to produce updated images and has captured over 10 million miles of footage worldwide (Google™, n.d.a). Other technology companies now provide similar services, such as Apple’s Look Around, Microsoft’s Bing Streetside, Meta’s Mapillary, and KartaView (Apple™, 2025; KartaView™, 2025; Meta™, 2024; Microsoft™, 2025).

Around 2010, researchers began using GSV to assess the reliability of built neighborhoods, conducting environmental audits (Badland et al., 2010; Clarke et al., 2010; Rundle et al., 2011). While one review of street view services in health research found that most of the 54 articles used street view services to assess the built neighborhood (Rzotkiewicz et al., 2018), other studies have used street-view services to explore neighborhood perceptions and experiences. For example, one pilot project examined a virtual reality walk-through interview with GSV to review physical environment fragmentation and found the interview evoked emotion and dialogue about the environment (Kostakos et al., 2019). Another study used GSV to complete digital go-along interviews to understand how familiar places are revisited virtually (Ghita, 2024). Ghita (2024) operationalized using GSV to examine the connections between memories and physicality in the digital space. Referring to the Baradian concept of spacetimemattering, which describes the entanglement of space, time, and matter (Barad, 2007), Ghita (2024) analyzed the use of digital places to shift between the past, present, and future, postulating that elements of spacetimemattering coalesce through GSV because of the memory of the place previously experienced physically. Given the extensive strengths of research utilizing go-along interviews and street view services, there exists a need for novel methods that utilize these strengths while addressing their limitations.

Description of the Go-Along Virtual INTERview Method

The Go-along Virtual INTERview (GVIv) is a nursing-led, participant-guided qualitative approach that uses digital street view services to conduct virtual go-along interviews and explore the neighborhood environment. Through the virtual neighborhood navigation, the GVIv method addresses certain limitations and data collection concerns associated with in-person go-along interviews. Similarly, by integrating individuals’ perspectives through in-depth and curated qualitative inquiry, the GVIv method addresses the limitation of street view services and tools, such as the lack of nuance, temporality, and lived meaning missed when using only online platforms, while maintaining the strength of in-depth qualitative interviewing that centers participants’ perspectives and interpretations of place.

Our proposed GVIv method builds on a foundation of proven epidemiologic approaches, underscoring the importance of guiding these types of neighborhood-based qualitative inquiries with relevant theoretical perspectives (e.g., the Political Ecology Framework), a clear ontological and epistemological stance (such as pragmatism and social constructionism, both inherent to this form of inquiry), and principles of community engagement (Israel et al., 1998). The following sections discuss our applied use of this guidelines in more detail, including a case study of the GVIv method and procedures implemented in a pilot study examining neighborhood-level influences on opioid use and recovery in four neighborhood areas in Chicago, Illinois.

Political Ecology Framework

First introduced in the 1930s and further developed in the 1980s, the Political Ecology Framework examines how history, culture, and the political economy shape the complex conditions within which people live and can create inequitable environments that may lead to poor health outcomes (Chitewere et al., 2017; Mayer, 1996; Robbins, 2012). This framework applies to contemporary neighborhood and health research by examining the underlying factors influencing social and physical environments, producing and maintaining health disparities (Chitewere et al., 2017). Increasing understanding of how neighborhoods can impact health outcomes requires unraveling the complexities of inequitable environments and applying the findings to improve population health. Key constructs of the Political Ecology Framework provide a foundation for understanding the pillars of inequitable person-made environments to offer ideas for positive change. For this pilot study using the GVIv method, we modified the neighborhood Political Ecology Framework to include the reciprocal relationship between the social and built environments to guide the semi-structured interview with broad, open-ended questions that explored the neighborhood’s history, economy, culture, and social and built environments (see Figure 1). Depending on the study’s purpose and aims, other theoretical frameworks, such as Bronfenbrenner’s Ecological Systems Theory (Bronfenbrenner, 1979), the Social Capital Theory (Carpiano, 2006), the Community as Partner Theory (Anderson & McFarlane, 2020), and the Social Ecology Systems Framework (Ostrom, 2009), may apply to the GVIv method.

Figure 1.

Modified political ecology framework.

Social Constructionism

As the participant virtually guides the researcher through the neighborhood using a street view service, the GVIv dynamic integrates the philosophical approach of social constructionism by exploring the experiences and perspectives that are co-constructed socially (Berger & Luckmann, 1966; Crotty, 1998; Gergen & Gergen, 2004). Through the participants’ views within the historical and cultural context (Crotty, 1998), the GVIv method uses broad context-defining questions to better elucidate place-based processes and interactions. Guided by social constructionist philosophy, the researcher aims to understand the participants’ meaning of their experiences and perceptions, discovering how history, culture, and social interactions shape these personal constructions (Berger & Luckmann, 1966; Crotty, 1998; Gergen & Gergen, 2004). The social constructionist philosophy aligns with a relational approach to neighborhoods, which reviews processes, networks, interactions, and underpinnings between people and neighborhoods (Cummins et al., 2007). This relational approach supports the perspective of neighborhoods as robust and interactive relationships between people and places (Cummins et al., 2007). Examining experiences through a social constructionist lens provides a way to describe this relational approach to understanding places and how people live in their neighborhoods (Anderson & Jones, 2009; Cummins et al., 2007; Davidson et al., 2008).

Pilot Application and Methodological Insights From a Nursing-Led Study

Study Procedures

To demonstrate the feasibility and practical application of the GVIv method, we implemented it within an ongoing nursing-led qualitative pilot study examining neighborhood-level influences on opioid use and recovery in four neighborhood areas (Austin, East Garfield Park, North Lawndale, and West Garfield Park) in Chicago, Illinois. This pilot was embedded within a larger parent study, with substantive findings to be reported in a separate manuscript. This nursing-led study leveraged community-engaged research (CEnR or CER) as a modality to enhance the perspective behind the GVIv approach. Our goal was to examine neighborhood-level influences of opioid use and recovery through experiences and perceptions of people who have a history of opioid use disorder.

Hence, the primary purpose of this pilot was methodological and aims to refine and assess the procedural, ethical, and analytic components of the GVIv approach. The sections below describe, in a step-by-step manner, how this method was operationalized, the community-engaged processes that guided implementation, and proposed data management and analysis strategies using an eight-step approach:

Step 1. Preparatory and Community-Engaged Design

Institutional Review Board approval and recognizing the community as a unit and working to facilitate collaborative involvement of all partners (Israel et al., 1998), the study team built on long-standing relationships and included a community advisory board (CAB. n = four members) of peer recovery support specialists, who lived in or were familiar with the same neighborhoods of interest, to help guide the GVIv method. The CAB guided study implementation, advised on the interview protocol, and reviewed cultural and contextual fit of study procedures. These recommendations directly informed revisions to study materials and procedures described next. Table 2 summarizes how CEnR principles informed GVIv adaptation.

Table 2.

Principles of Community-Engaged Research.

Principles	Operationalization
Recognize community as unit of identity	• The lead author (LA) volunteers in the recovery center in one of the studied communities for the last seven years. • The team recognizes and respects recovery community’s shared values and symbols (ex: LA frequently attends monthly graduation ceremonies and recovery meetings). • The team listens to community, remains open, and asks questions.
Build on strengths and resources within community	• The research aims to understand neighborhood level influences of opioid use to guide place-based interventions and inform equitable policies that can support social structures and processes. • Through explaining the study’s purpose, providing a convenient location of research, utilizing the GVIv method to avoid re-experiencing neighborhoods of opioid use, the team draws on community strengths and attempts to mitigate harm to community.
Facilitate collaborative, equitable involvement of all partners in all phases of research	• For the pilot study, the LA formed Community Advisory Board (CAB) with four certified peer recovery support specialists. • The LA met with each member for 1 hr and discussed research topic, method, problem definition, protocol and accepted all feedback from CAB. • Although not initially planned, the CAB members volunteered to assist with recruitment.
Integrates knowledge and action for mutual benefit of all partners	• The aims of proposal are to guide action and create new understanding that can drive change. • There is a commitment to integrating the results and improving services and policies in communities.
Promotes co-learning that attends to social inequalities	• The LA recognizes social and economic marginalization of people with a history of opioid use, and how the intersectionality of racism and sexism further compounds this discrimination. • The LA considers her identity as a White, non-Hispanic woman without a history of opioid use and how this alters the research. • Through working and volunteering in recovery center and surrounding food pantry, the LA has a long-standing commitment to learn about lived experience of community members.
Involves a cyclical and iterative process	• The research topic was developed through long-standing conversations with community members. • The development of the GVIv research method was modified to address physical and emotional safety concerns.
Addresses health from positive and ecological perspectives	• The research works to improve health, through focusing on neighborhood domain of ecological model. • The research pursues a focus that addresses systemic inequities.
Disseminates findings to all partners	• The team will provide an overall summary of findings in language that is understandable and recognizes community ownership of knowledge.
Involves a long-standing commitment	• The team will continue to maintain long-standing relationship with community and partnerships developed over time.

Source. Principles from Israel et al. (1998).

Step 2. Interview Guide Development and Theoretical Anchoring

A semi-structured interview guide was actively developed with our CAB, centering key constructs of the Political Ecology Framework as a holistic and relevant theoretical anchor (Figure 1), addressing the neighborhood’s history, culture, social, and built environments. Early in this process, one CAB member stressed the importance of explaining the motivation behind the research to community members and another member suggested adding a question about what the participants would like to see changed in their neighborhood, centering solutions alongside problem identification. Two CAB members stressed capturing non-static and temporal changes over time in the neighborhood. One CAB member stated, “Let’s hit the roots!” and explained that understanding people’s perspectives of their neighborhood history is essential.

Step 3. Participant Recruitment, Screening, and Consent

Interested participants were provided with a standardized study introduction that explained the purpose of the research and the GVIv procedure, consistent with CAB recommendations. Participants who expressed continued interest accompanied the lead author to a private room within the recovery center to complete eligibility screening. Eligible participants provided written informed consent prior to participation. All recruitment, screening, and consent procedures were conducted by the lead author to ensure consistency.

Step 4. GVIv Session Setup and Technical Requirements

We provided a scripted study introduction that incorporated the motivation behind the research, as suggested by the CAB, to any interested study participant. If participants remained interested, they accompanied the lead author (LA) to a quiet room in the recovery center to complete the screening process. If eligible, the LA completed the informed consent. The GVIv method requires a computer with a web browser, a quiet room, and a stable and secure internet connection. The setup included two chairs positioned toward the shared screen, note paper for both participant and LA, and a procedural checklist to ensure standardization. These requirements were documented to support reproducibility of the method in other settings.

Step 5. Participant-Led Virtual Neighborhood Walkthrough

Each session began with rapport-building and brief background questions. Following participant consent, the LA started Microsoft Teams™, and, with the camera off, began recording the screen and transcript. After asking the background questions, the LA launched the street view service (e.g., Apple Look Around, Google Street View, KartaView, Meta Mapillary, or Microsoft Bing Streetside) and shared the screen with the Microsoft Teams™ meeting. The LA asked the participant to identify an intersection or location they frequented during the time they used opioids. Once the location was verified, the LA facilitated a participant-led virtual “walk” through the neighborhood using the semi-structured interview guide. Questions were designed to elicit narratives about built, social, economic, cultural, and temporal dimensions of neighborhood experience, guided by the Political Ecology Framework (Chitewere et al., 2017). When available, archived street-view images were used to revisit earlier neighborhood states, deepening reflection on environmental change and continuity. Using the stored images, the LA found the image taken within the closest year to when the participant frequented the neighborhood.

Of note, during sessions, the researcher intentionally shifted between facilitator, follower, and guide roles based on participant needs, technical demands, and session flow. For example, the researcher primarily acted as a follower, paying close and uninterrupted attention when participants navigated the virtual environment and narrated their experiences, allowing participants to set the pace, direction, and points of emphasis. The facilitator role was adopted to support narrative and explanatory continuity through neutral prompts, clarification questions, or reflective probes aligned with the pilot’s interview guide. The guide role was used sparingly and only when participants requested assistance with navigation, encountered technological difficulties, or required redirection to maintain alignment with the study aims. These roles are vital for the involved nature of the GVIs methods and can shifted as needed but must aim to preserve participant leadership while ensuring procedural consistency and data relevance.

Step 6. Data Capture and Storage

Audio and visual data were recorded through Microsoft Teams™ and automatically uploaded to a secure, encrypted institutional server. Screen recordings captured both participant narration and the virtual neighborhood walkthrough. All files were labeled using standardized identifiers to link transcripts, audio, and visual data while maintaining confidentiality.

Step 7. Data Preparation and Quality Assurance

Each GVIv session lasted approximately 60 min and was recorded via Microsoft Teams™. Audio and visual data were automatically uploaded to a secure, encrypted institutional server. The Teams-generated transcripts were verified against the recordings and edited for accuracy, while screenshots of key street-view images were matched to corresponding transcript segments. This process created a layered and triangulated dataset that combined narrative, visual, and spatial information. To preserve methodological transparency, the research team documented procedural fidelity, noting session flow, participant engagement, and any technological or ethical challenges encountered. These reflections were used to refine the procedural checklist and improve future GVIv implementation. The data preparation process established a structured foundation for subsequent coding and analysis once the full study is complete.

Step 8. Data Analysis Strategy

The GVIv method enables the triangulation of both visual and audio data, which can be analyzed to explore participants’ experiences and perceptions. While the data analysis approach will depend on the research question and aims, we propose a plan that utilizes codebook thematic analysis, employing a hybrid inductive and deductive approach to analyze the transcript and visual data. While we present a proposed data analysis strategy, we recognize this is a novel method and the data analysis strategy will evolve with ongoing implementation.

Our analytic plan is also rooted in a pragmatic epistemological lens, emphasizing inquiry that connects knowledge generation to actionable solutions (Younas, 2020). Pragmatism aligns with nursing’s applied focus and with the Political Ecology Framework (PEF), which situates individual experience within larger historical, cultural, and political contexts. The pragmatic epistemological lens guides the codebook thematic analysis method, which combines aspects of reflexive thematic analysis (Braun & Clarke, 2022b) and a more structured coding procedure (Braun & Clarke, 2023). Given the extensive visual and transcript data collected using the GVIv method, codebook thematic analysis can provide a structure (i.e., thematic matrix) that creates consistency and upholds rigor within the analysis, while also allowing flexibility to interpret patterns of meaning within the qualitative data (Braun & Clarke, 2022a). Using this foundation, we recommend codebook thematic analysis (Bingham, 2023; Braun & Clarke, 2022a) as a flexible yet structured method suited to the hybrid data that GVIv produces, recognizing the importance of understanding the perspectives of those with lived experiences through their eyes (Gray, 2021), and to facilitate synergistic and seamless synthesis of visual and transcript data.

This hybrid inductive-deductive coding approach uses deductive coding to organize the visual/neighborhood data and inductive coding to describe patterns or themes from participant-led walkthroughs (Bingham, 2023; Bingham & Witkowsky, 2022). First, a codebook with pre-defined, deductive codes and emergent, inductive codes will be created (Bingham & Witkowsky, 2022; Braun et al., 2016). For example, a priori codes derived from the Political Ecology Framework (such as social environment, built environment, culture, and history) can guide deductive codes, helping situate the neighborhood risks, resources, and perceptions. The images and transcript can be analyzed separately using inductive coding before identifying relationships between the codes and developing themes to form a unified thematic matrix (Chapman et al., 2017). Second, the deductive and inductive codes derived from the images, text, and combined data will be aggregated in the codebook (Chapman et al., 2017). To ensure rigor, multiple coders will review and agree on the assigned codes, thereby creating intercoder agreement (Braun et al., 2016). Third, the codebook will be examined for patterns and supporting data to create themes. Theme development will be reviewed for coherence and supported by the dataset (Braun & Clarke, 2006).

In keeping with community-engaged research principles, findings must be shared and discussed with the CAB to receive feedback regarding the study outcomes, in addition to member checks. Using study journals, memoing, and team discussion, reflexivity is integrated during inductive coding, code review, and theme development. Transcripts are maintained and include exact wording, giving the ability to refer to original data with any questions. Through this analytic process, GVIv data can illuminate the relational and contextual mechanisms linking neighborhood environments and health, thereby advancing the nurse researcher’s capacity to investigate place-based inequities through a rigorous, reflexive, and community-driven approach. While the specific analytic approach may vary by research question, the procedural steps described above constitute the core components of the GVIv method and can be adapted across qualitative paradigms and substantive areas.

Pilot Study Evaluation: Feasibility and Lessons

Six participants completed the pilot study. CAB members initially helped recruit, while snowball sampling began after the first two participants. There was no difficulty with recruitment, and since the pilot study was capped at 6 participants, 12 participants were placed on a waitlist for a subsequent study. All visits were completed the same day as informed consent, and there were no incomplete study visits. Methodological evaluation was conducted by documenting procedural fidelity using standardized checklists, recording technological performance and disruptions during sessions, tracking participant engagement and navigation of the virtual environment, and maintaining reflexive study journals to capture ethical considerations and implementation challenges. Transcript accuracy, visual–text linkage, and data integration processes were systematically reviewed following each session to assess feasibility and inform iterative refinement of the GVIv procedure.

While the findings of the pilot study will be incorporated into a larger study and reported in another manuscript, we evaluated the GVIv method using eight areas proposed by Bowen et al. (2009). Table 3 outlines each area with examples from our pilot study, lessons, and limitations. The pilot study’s preliminary insights underscore the practicality, ethical integrity, and participant-centered design of the GVIv method. The pilot study not only demonstrated GVIv’s feasibility in community-based settings but also informed key refinements to the interview guide, analytic framework, and strategies for sustaining equitable community partnerships. Together, these insights position GVIv as a scalable, nursing-led innovation that advances qualitative inquiry into place, health, and equity. Future research can leverage GVIv to systematically generate, analyze, and translate data across diverse nursing and community health contexts, enhancing the discipline’s ability to address environmental and structural determinants of health through contextually grounded, person-centered methods.

Table 3.

GVIv Evaluation and Lessons.

Area of evaluation	Pilot study insight, lessons, and limitations
Acceptability: Is the GVIv approach acceptable to researchers and participants?	Pilot study insight: • Successful recruitment of CAB members (n = 4) and study participants (n = 6) • All eligible participants participated in entire study visit • Participants referred peers to study • Pilot concluded with no events of participant distress, discomfort, or ending interview early
	Lessons: • Straight forward, easy to use approach for researcher and participants • Overall perceived interest by participants to discuss their neighborhoods
	Limitations: • Attractiveness of approach may depend on participants’ connection and engagement with their neighborhood
Demand: How likely is the GVIv approach to be used?	Pilot study insight: • Demand for pilot increased after two visits • Pilot concluded with waiting list of interested participants (n = 12) • Repeated sentiment from CAB and participants that enjoy talking about neighborhood
	Lessons: • Permits exploration of large geographic area in 60–90 min visit • Offers adaptable qualitative geo-focused approach
	Limitations: • Limited to research focused on place-based topics
Implementation: How can GVIv be implemented?	Pilot study insight: • GVIv method implemented and understood by participants with short introduction and explanation • Participants who did not wish to return to neighborhood had no difficulty with virtual navigation
	Lessons: • Time spent explaining purpose of study and procedures required for smooth implementation • Checklist used to ensure recording of screen is captured • Accounts for psychological safety for participants not wanting to return to area
	Limitations: • Technical difficulties or malfunctions can interrupt study visits or data collection • Requires participants’ ability to remember neighborhood or place
Practicality: What resources and circumstances are needed to use the GVIv approach?	Pilot study insight: • Pilot study implemented successfully with all visits around 60 min • Quiet room, internet, and computer necessary for study visit • Screenshare was not captured during part of one participant visit due to researcher forgetting to record • Minimal costs (supplies, gift card for participants)
	Lessons: • Cost-effective and time-efficient • Virtual navigation controlled for weather or terrain issues • Participants with difficulty ambulating able to complete the study
	Limitations: • Requires computer, stable access, and access to neighborhood through street view services
Adaptation: How can GVIv be used with different populations, locations, or study topics?	Pilot study insight: • Utilized a semi-structure interview guided by PEF framework and successfully adapted approach to the pilot study • Did not compare GVIv pilot with interview-only control
	Lessons: • Easily adapted with different population (e.g. patients with cardiovascular disease, adolescents, older adults) • Could be expanded to examine multiple health-related factors (e.g. environmental exposures, walkability, green space) • Global and rural adaptation dependent on available street view services • Greater number of images in urban areas, holding possibilities for urbanicity studies • Provides opportunity to study locations virtually from any location in the world
	Limitations: • Not all areas have the same number of images. • Images may vary depending on street view service. • Rural areas may not have as many images as urban areas.
Integration: How can GVIv be integrated within a study?	Pilot study insight: • Pilot study centered on opioid use and neighborhood experiences and perceptions • GVIv approach generated large amount of rich data
	Lessons: • Could be used independently or to supplement growing geo-focused quantitative methods for mixed-method study • Could implement with focus group to explore neighborhood collectively
	Limitations: • No limitation noted
Expansion: How can the GVIv approach be expanded to create another approach?	Pilot study insight: • The pilot used the GVIv method as the sole method.
	Lessons: • GVIv could be expanded to be used with asset mapping
	Limitations: • No limitation noted
Limited-efficacy testing: Does GVIv meet the study aims?	Pilot study insight: • GVIv’s ease to implement and ability to elicit dialogue about neighborhood perceptions and experiences and opioid use met study aims
	Lessons: • GVIv holds possibility to explore complex interactions between health and place globally • Visualization of neighborhood led to initiating memories • Evokes discussion of neighborhood experiences and perceptions
	Limitations: • Large amount of generated data may be challenging to organize for analysis

Source. Areas of evaluation from Bowen et al. (2009).

Researcher Feedback

In addition to the CAB members, the pilot’s aim to learn about neighborhood experiences and perceptions drew the interest of many participants. The majority of the participants expressed unsolicited enjoyment in virtually navigating and discussing their neighborhood. Five of the six participants navigated the neighborhoods swiftly, answering questions, and discussing experiences and perceptions. However, not all participants discussed their neighborhood experiences and perceptions with the same interest, reflection, or enthusiasm. One participant needed more prompts to reflect on experiences and became confused while navigating the streets, often not remembering certain areas. Reasons for this difference were not explored, but could include difficulty remembering due to memory or substance use, frequent movement between neighborhoods, shorter duration in the neighborhood, or differences in spatial understanding.

The street view service consistently navigated the streets, including alleys, ensuring we could access any physical location of interest to the participant. One unexpected finding was the temporal aspect, which was repeated at every visit. Our semi-structured interview inquired about changes in the neighborhood, and participants spoke about the temporal shifts they experienced. This element was enhanced by street view service features that stores older pictures and allowed the participants to explain stores or buildings that no longer existed. Two challenges noted with older images were (1) not all streets had the same number of older images, and (2) the year of the image could switch as we navigated the streets. For example, if we moved down one street using the 2018 image, and then turned down a side street where there was no image in 2018, the screen would jump to the image taken in a different year. Since the images were used to discuss neighborhood experiences and perceptions, this did not cause shifts in the interview; however, it is noted as a limitation.

In addition to the lessons noted in this pilot study, we wish to highlight the depth of the data generated by the GVIv approach. Three sources of data resulted from the GVIv visit: (1) the interview transcripts, (2) the visual street view service images, and (3) the virtual go-along route. As the data analysis suggests, using the interview and street view service images can provide robust insight. However, to create a more standardized way of capturing the crucial images, we suggest asking participants to note the images that are essential to them. This will ensure the researcher is not inferring significance when it is not present for the participant. Geographic information systems (GIS) analysis can capture the virtual go-along route. We recognize that the use of this data will depend on a study’s aim and urge researchers to consider how they will use the large amount of data during the study’s development.

Discussion

This study introduces the Go-along Virtual INTERview (GVIv) as a novel, nursing-led qualitative method which meaningfully operationalizes the nursing metaparadigm within a digital spatial context. GVIv is grounded in nursing’s commitment to understanding the whole person within their environment, enabling researchers to examine how structural, social, and physical neighborhood conditions influence health and healing while maintaining participant safety and agency. By integrating street-view navigation enriched by semi-structured qualitative feedback, GVIv creates a co-constructed encounter in which the neighborhood itself becomes a participant in the conversation, reflecting nursing’s relational and ecological worldview. In addition to the presented lessons and limitations (Table 3) based on a pilot of the method in an opioid use disorder-focused study in Chicago, we will conclude with a discussion about street view services data use considerations, implications for nursing science, applications for GVIv, and ethical considerations.

Data Use Limitations and Considerations

While reviewing the propriety laws for each street view service is beyond the scope of this manuscript, we note that certain image sources, like Google Street View, Apple Look Around, and Bing Streetside, limit storing, analyzing, and reproducing images (Apple™, 2024; Google™, n.d.b; Microsoft™, 2024). Other sources have more permissive policies, such as Mapillary, which uses Creative Commons licensing, and KartaView, an open-source project (KartaView™, 2025; Meta™, 2024). Given the extensive use of street-view services in urban GIS research (Biljecki & Ito, 2021), the discussion of proprietary laws is not new to the literature (Helbich et al., 2024). As seen in the review of literature, Google Street View is the most commonly utilized street view service (Biljecki & Ito, 2021; Rzotkiewicz et al., 2018), and Helbich et al. (2024) called for Google Street View to provide licensing for academic purposes. While the use of street view services for the GVIv method differs from their use in GIS, we recognize that proprietary laws vary across services and countries and urge researchers to review street image policies before implementing the GVIv method. Additionally, we second the previous call for licensing for academic use of Google Street View (Helbich et al., 2024).

Implications for Nursing Science and Practice

The GVIv method advances nursing methodology by translating core metaparadigm elements (person, environment, health, and nursing) into an actionable qualitative approach that treats the environment as an active participant in assessment and meaning-making. Nurse researchers and community or public health nurses can apply GVIv to systematically identify place-based facilitators and barriers to health, including safety, access, stigma, and social cohesion, using participant-guided navigation to anchor inquiry. This participant-led design centers equity and partnership, rebalancing power dynamics and aligning with nursing’s relational and advocacy ethics while integrating community-engaged research practices that ensure accountability to residents. The virtual “walk-along” structure also enhances feasibility and safety by eliminating weather, mobility, and neighborhood access constraints inherent in traditional go-along methods, yet preserves the ecological validity and narrative richness essential to nursing assessment. Findings derived from GVIv can inform upstream, neighborhood-responsive interventions, enrich community health and qualitative methods curricula, and provide visual-narrative evidence to guide health policy and urban planning collaborations. Finally, coupling GVIv with GIS and spatial epidemiology enables nurses to connect lived experience with population-level patterns, strengthening translational pathways from narrative insight to actionable health equity solutions.

Application of GVIv

The impact of neighborhood on health and health behavior is complex and nuanced. Physical structures shape individual and group behavior in non-linear, synergistic, and iterative ways. Illuminating this complexity requires sophisticated methods. Foundational neighborhood research focused on examining associations between neighborhood characteristics and health outcomes, such as census-level poverty data and disease risk. Early neighborhood and health research consisted of cross-sectional studies that used census-based data (Diez Roux & Mair, 2010). Current place-based research increasingly leverages quantitative measures of the built environment, given advances in spatial data availability, GIS, and spatial epidemiological methods, and computing power. Additionally, the field of neighborhood-health research has grown from examining solely singular measures, such as crime rates or greenspace, to also include comprehensive index-based measures that capture holistic neighborhood environments.

However, quantitative neighborhood research, while powerful, can be supplemented by qualitative methods (such as GVIv) to illuminate neighborhood phenomena that cannot be captured quantitatively, such as socially constructed, perceived, and or individualized influences of neighborhood on health. GVIv may be particularly powerful when combined with advanced quantitative spatial research, because the GIS research can capture population-level phenomena while GVIv captures and gives voice to the lived experience of individuals experiencing those phenomena – combining for a comprehensive overview. A GIS-GVIv approach can be leveraged to center participant’s realities and highlight neighborhood residents’ priorities, needs, strengths, and goals, while contextualizing their stories within rich and powerful large-scale spatial data. Doing so would be consistent existing spatial methods that carefully consider power, representation, and justice-related issues of participants’ role in neighborhood research (e.g., critical GIS, spatial justice, post-colonial GIS; Pavlovskaya, 2018; Sheppard, 2005).

Ethical Considerations

Specific ethical considerations are worth noting with the use of GVIv, particularly when participants are members of marginalized populations and thus may be most sensitive to power inequities in research. Recognizing viewing images could evoke distress, we discussed this risk before the study, reminded participants that they could stop at any time, and ensured they had recovery support. A Certificate of Confidentiality was obtained for this study because of its focus on past substance use. Even though the researchers were not in the neighborhood, participants may be more comfortable disclosing certain activities with the protections of a Certificate of Confidentiality. Similarly, using cross streets instead of specific addresses helps maintain privacy; doing so may be necessary in studies that require identifying information (e.g., a participants’ home residence). Researchers interested in using GVIv for work that requires use of individual data will want to ensure careful attention to and insight from a collaborator who has expertise related to ethical implications of place-based research. Since our study examines neighborhood-level factors related to opioid use and recovery, specific addresses were not needed for the content of the study. Additionally, consistent with ethical principles of Community-Engaged Research approach and given the cocreation of study findings that results from GVIv, researchers must be proactive about ensuring that (1) results are shared with participants and their stakeholder groups and (2) that action is taken based on the findings (beyond academic publishing) to ensure meaningful change in line with participant goals. The visual and story-based results that result from GVIv would be particularly well-suited to doing both in a compelling manner.

Conclusion

Understanding how neighborhoods influence health outcomes can inform nursing research, guide the development of equitable policy, and support community-driven interventions. The GVIv method offers an innovative modification of the traditional qualitative go-along interview to explore neighborhood and health data, providing a more robust approach to examine nuanced neighborhood factors and conduct thorough analyses. Supported by the Political Ecology Framework, the semi-structured interview works to explore the processes and interactions at the neighborhood level. Led by qualitative descriptive theory, recording audio and visual data provides the opportunity for robust thematic analysis. The GVIv method offers new possibilities for qualitative research exploration of places that unveil relational aspects of neighborhood and health.