A phenomenological understanding of residents’ emotional distress of living in an environmental justice community

Abstract

Deteriorative environmental conditions in environmental justice (EJ) communities not only post direct health risks such as chronic illnesses, but also cause emotional distress such as anxiety, fear, and anger among residents, which may further exacerbate health risks. This study applies a descriptive phenomenological method to explore and describe the emotional experience of residents living in Ironbound, a known EJ community located in Newark, New Jersey. Twenty-three residents participated in the study. Four essential themes regarding the residents’ emotional experiences were elicited from 43 interviews: (1) being worried about the harmful effects of the surrounding pollution; (2) being distressed by the known historical pollution sources; (3) being frustrated by the unheard voices and/or lack of responses; and (4) being angered by the ongoing pollution sources. Participants not only expressed their emotions of worry, distress, frustration, and anger in detail but also described reasons or situations that provoked such negative emotions. Such detailed depictions provide insights into potential meaningful strategies to improve residents’ psychological wellbeing by alleviating negative emotions and meaningfully engaging residents in developing, implementing, and enforcing environmental laws, regulations, and policies to achieve EJ goals.

Barriers to Type 2 Diabetes Management Among Older Adult Haitian Immigrants

“…Demographic data were analyzed using IBM SPSS Statistics for Windows, Version 26 (IBM Corp, Armonk, NY, USA). To ensure the credibility of data analysis, we used a modified iterative 7-step descriptive data analysis method [22][23][24][25] to examine data, compare codes, challenge interpretations, and develop themes inductively. The steps were (1) reading the transcripts several times to gain a broad understanding of the text, (2) meeting as a group to identify key quotations and discuss key codes related to the research question, (3) combining the coded quotations into 1 file and confirming the accuracy of the code and quotation, (4) carefully analyzing quotation files and identifying major themes by collating key coded quotations for each research question, ( 5) meeting as a group to review major themes and engaging in active dialogue to resolve any discrepancies, (6) reviewing the transcripts and validating the structure of themes alongside interview data, and ( 7) conducting multiple discussions until consensus was achieved about each aspect of the process of data analysis.…”

Suburban agriculture, immigrant farmers, and access to agricultural services and resources

“…Following a descriptive phenomenological approach (Dory et al, 2017;Fu et al, 2008), semistructured interviews were conducted with the Chinese immigrant farmers to understand their farming experiences of accessing agricultural services and resources. A key step in applying descriptive phenomenology is not to introduce bias and preconceptions to participants.…”

“…A key step in applying descriptive phenomenology is not to introduce bias and preconceptions to participants. Therefore, the phenomenological reduction strategy of "bracketing" was adopted in the interview question design and interviewing process, in which existing knowledge and researchers' personal understanding of farming experiences was intentionally bracketed out (Denzin, 1989;Dory et al, 2017;Fu et al, 2008Fu et al, , 2009. The interview questions were carefully designed without directly asking the participants whether they can access agricultural services and resources.…”

“…Participant recruitment and interviews occurred between June and August 2017. Following Dory et al (2017), each interview lasted from 40 to 120 minutes and was recorded using a digital audio device. Observational data for each participant were also recorded.…”

“…Crucial to this method is systematically classifying data into fewer content-related themes that share the same meaning, which are coded in color and text (Dory et al, 2017). We followed the seven-step data analysis procedure described by Dory et al (2017) and Fu et al (2008Fu et al ( , 2009 to examine data, compare codes, challenge interpretations, and inductively develop themes.…”