Relational Drivers: Qualitative Analysis Help-Seeking

The PLOS ONE study by Connell et al. (published July 30, 2026) PLOS ONE shows that help-seeking for mental health and substance use among people under community justice supervision is not purely individual, it is shaped by social networks and local context.

• Connell et al. interviewed 50 people between November 1, 2023 and July 31, 2024, with 23 participants in LA1 and 27 in LA2, confirming relational influences across networks.
• Connell et al. report that nearly all participants self-identified as having current mental health or substance use needs and most had at least one unsuccessful help-seeking attempt as of July 30, 2026.
• Connell et al. identify three interacting domains that determine help-seeking: desire to seek help, ability to seek help, and help-seeking context; interventions need to operate at individual, network, and community levels.
• Participant testimony in Connell et al. includes stark phrasing such as "you just kind of can’t get away from the social" (Participant P13, Connell et al., 2026), illustrating network contagion and normalization effects.

Answer: The PLOS ONE study conducted in-depth qualitative interviews to map how social networks influence help-seeking among people in contact with the criminal justice system living in the community.

According to Connell et al. in PLOS ONE (published July 30, 2026), the researchers applied framework analysis to interview data collected from November 1, 2023 to July 31, 2024 and combined that with participant-aided social network diagrams using Network Canvas.

Connell et al. (PLOS ONE, 2026) recruited 50 participants (23 in the largely rural LA1 and 27 in the urban, deprived LA2), transcribed interviews verbatim, and coded data into three main themes: desire, ability, and context.

Connell et al. report that relational influences appear in both the "desire to seek help" and the "ability to seek help, " and that network-level mistrust and stigma frequently inhibited access to services.

Direct participant quotes in Connell et al. illustrate these dynamics, for example, "I have dealt with it for almost twenty odd years by myself… I don’t like going to doctors because I think they are a waste of time" (Participant P12, Connell et al., PLOS ONE, 2026).

Answer: Researchers should design qualitative studies that capture network-level data, not just individual narratives, because Connell et al. (PLOS ONE, 2026) show relational influences are central to help-seeking.

Connell et al. recommend multi-level interventions that address individual attitudes, network norms, and community-level service experience; qualitative evaluation should therefore code at the person, tie, and context levels.

Connell et al. (PLOS ONE, 2026) found that network mistrust and shared norms can normalize substance use and deter help-seeking, so evaluators need methods that detect contagion, homophily, and resource flows within networks.

For program design, Connell et al. suggest trauma-informed, relational practices and inclusion health approaches to improve service acceptability, particularly where networks share epistemic mistrust of statutory services.

Answer: Connell et al. (PLOS ONE, July 30, 2026) show that to understand help-seeking in community justice populations researchers must measure relational and contextual forces as well as individual factors.

Evidano helps teams operationalize that recommendation by linking transcript-level coding to network-aware visualizations and secure quote management, reducing manual synthesis time and improving transparency.

If you run qualitative or mixed-methods studies on help-seeking, consider ingesting your transcripts and network data into Evidano to generate thematic matrices and tie-level extracts that mirror the framework used by Connell et al.

Start a trial and see how AI-assisted thematic and network analyses speed interpretation and reporting: Try Evidano for free.