**3. Methodology**

We adopted collaborative autoethnography (Chang et al. 2013) as our methodological framework in this chapter. Collaborative autoethnography is a qualitative research method that is simultaneously collaborative, autobiographical, and ethnographic (ibid). Autoethnography, without the term "collaborative", is a combination of autobiography—the study of self-experiences and ethnography—a study of habits and culture. Autoethnography is an intriguing method that is increasingly utilized to study social phenomena through the lens of the author/researcher's personal experience (Wall 2016), although it is criticized for being self-indulgent, narcissistic, introspective, and individualized (Atkinson 2006; Wall 2016).

Chang et al. (2013) added the element of "collaboration" to the concept of autoethnography. Roy and Uekusa (2020, p. 384) argued that this method is convenient and ideal for qualitative researchers during "unprecedented times", in which conventional methods of collecting data are either disturbed by disasters or other limitations. Likewise, we selected this method in order to journal our professional and personal lessons, as social work academics, during the floods response that was compounded by COVID-19 in South African townships. This method was also chosen as it is aligned with the approach and processes adopted by a team of academics, interns, and students who organized themselves into a flood response team. We, the chapter's authors, are also members of a larger response team that reports to the Institute (MA'AT).<sup>1</sup>

As authors, we collaborated beginning with the identification of the flood-affected communities and continuing through the MA'AT's planning of the flood responses. This comprised the procedure for requesting entry permission from the appropriate ward councilors, "*izinduna*" (traditional headmen), and other crucial role players.

Collaborative autoethnography afforded us an unconventional opportunity to become researcher–practitioners. A crucial component of our flood response journey as reflective professional social workers was taking notes and keeping journals about

<sup>1</sup> Inspired by working with vulnerable communities in more than 10 African countries, the MA'AT Institute was established within the School of Applied Human Sciences within the College of Humanities of the University of KwaZulu-Natal, South Africa, to specifically advance Afrocentric thoughts and the provision of African-centered psychosocial services to communities experiencing adversities. The services of MA'AT are multidisciplinary and often involve social work academics, social work interns, social workers, educational and clinical psychologists and psychology interns.

our intervention methods. Notably, as part of our own debriefing sessions, after contact with the community, we comparatively journaled our experiences through a self-reflexive method. Authors such as Chang et al. (2013) and Roy and Uekusa (2020) have critically discussed collaboration's advantages and adverse dynamics during autoethnography. Specifically, Chang et al. (2013) argued that self-reflection methods such as autoethnography are popular due to their individualized approach, which is also likely to expose the author's vulnerabilities. The comparative reflections allowed for the multiple voices and perspectives to be included in the research, and this increased the source of data and information from a single researcher to multiple researchers (Roy and Uekusa 2020). As a result, the comparative perspectives this heightened the rigor of the information we recorded in our journals. Unlike single-authored autoethnographies, as collaborative auto-ethnographers, we combined our energy and data to create a richer pool of data from multiple sources (ibid).

Adapting work by Chang et al. (2013), we designed Figure 1 below in order to highlight the circular steps of research design and the importance of collaboration during the process of this research method:

Based on Figure 1, we followed a similar pattern to journal our reflections, as the main source of data, systematically and collaboratively. We needed to employ collaborative autoethnography due to its postmodernist lens, which rejects the generally accepted intellectual assumptions of knowledge generation and makes room for nontraditional ways of knowing and knowledge generation (Wall 2016). Using this method, we could critically journal our collective experiences of responding to concurrent disasters, COVID-19 and floods, with indigenous communities in townships. Adapting work by Chang et al. (2013), we designed Figure 1 below in order to highlight the circular steps of research design and the importance of collaboration during the process of this research method:

**Figure 1. The Circular Iterative Process of Collaborative Autoethnography**. Source: Adapted from Chang et al. (2013, p. 24). **4. Lessons Learnt Figure 1.** The Circular Iterative Process of Collaborative Autoethnography. Source: Adapted from Chang et al. (2013, p. 24).

Based on Figure 1, we followed a similar pattern to journal our reflections, as the

room for nontraditional ways of knowing and knowledge generation (Wall 2016). Using this method, we could critically journal our collective experiences of responding to concurrent disasters, COVID-19 and floods, with indigenous

The comparative journaling that we performed after each intervention was carried out concurrently with the examination of our reflections. In order to collaboratively create meaning, we used the model from McPhail-Bell and Redman-Maclaren (2019) to categorize similar sentences into groups, produce codes on an individual basis, and then work together in person during meetings. We had sections for, among other things, reflections on our own vulnerabilities, reflections on the current condition of the community members, reflections on newly formed partnerships and alliances, reflections on collaborations, reflections on the community's reaction to our intervention, and so forth. This categorization of our reflections enabled us to learn lessons from each other and, lastly, to provide the below narrative accounts of our collective experiences. Moreover, the collaborative

communities in townships.

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analysis of our reflection ensured the trustworthiness of our findings.

The comparative journaling that we performed after each intervention was carried out concurrently with the examination of our reflections. In order to collaboratively create meaning, we used the model from McPhail-Bell and Redman-Maclaren (2019) to categorize similar sentences into groups, produce codes on an individual basis, and then work together in person during meetings. We had sections for, among other things, reflections on our own vulnerabilities, reflections on the current condition of the community members, reflections on newly formed partnerships and alliances, reflections on collaborations, reflections on the community's reaction to our intervention, and so forth. This categorization of our reflections enabled us to learn lessons from each other and, lastly, to provide the below narrative accounts of our collective experiences. Moreover, the collaborative analysis of our reflection ensured the trustworthiness of our findings.
