Network Insights: qualitative analysis help-seeking

According to the PLOS ONE article by Connell et al. (published 30 July 2026) PLOS ONE, help-seeking for mental health and substance use among people in contact with the criminal justice system is driven by interacting individual, relational, and contextual factors rather than being solely an individual choice. The PLOS ONE study found relational influences operate across social networks and local culture and that interventions should therefore span individual, network, and community levels.

• The PLOS ONE study interviewed 50 people between 1 November 2023 and 31 July 2024 and reported recruitment from two local authority areas with 23 participants in LA1 and 27 in LA2 (Connell et al., PLOS ONE, 30 July 2026).
• The study reports that 49 of 50 participants consented to verbatim quotation for research reuse in the UK/EU (Connell et al., PLOS ONE, 30 July 2026).
• Connell et al. (PLOS ONE, 30 July 2026) state plainly, “help-seeking is not an individual behaviour, but strongly affected by relational influences, ” highlighting network-level mistrust, stigma, and resource flows as core barriers.
• Interviews ran up to two hours, used participant-aided sociograms and Network Canvas for social network elicitation, and applied framework analysis with double coding to produce a reproducible thematic framework (Connell et al., PLOS ONE, 30 July 2026).

Answer: Connell et al. (PLOS ONE, published 30 July 2026) conducted 50 semi-structured interviews in two Scottish local authority areas between 1 November 2023 and 31 July 2024 to explore influences on help-seeking for mental health and substance use.

Connell et al. recruited via justice social work and third sector services, used Network Canvas to co-create participant sociograms during interviews, and transcribed interviews verbatim before pseudo-anonymisation. The authors then applied framework analysis in NVivo with independent double coding to generate three top-level themes: desire to seek help, ability to seek help, and help-seeking context (Connell et al., PLOS ONE, 30 July 2026).

Direct quotation illustrates the social constraint: Participant P12 said, “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, ” and Participant P13 said, “you just kind of can’t get away from the social, every time you try and do something good basically they will get you on it and you will be wrecked, ” both quoted in Connell et al., PLOS ONE (2026).

Answer: Connell et al. (PLOS ONE, 30 July 2026) show that qualitative studies of help-seeking must intentionally capture relational data and local context to produce operational findings for intervention design.

Practical implication 1: Elicit social networks during interviews. Connell et al. used participant-created sociograms and Network Canvas during interviews to reveal that network norms and resources determine whether help-seeking will be supported or suppressed.

Practical implication 2: Code for relational processes as well as individual attitudes. The PLOS ONE framework embeds relational sub-themes inside desire and ability rather than treating relationships as a separate, optional category.

Practical implication 3: Design multi-level interventions. Connell et al. recommend testing interventions that act at individual, network, community, and policy levels because networks can share epistemic mistrust and stigma that block individual change.

Answer: The PLOS ONE study by Connell et al. (published 30 July 2026) demonstrates that qualitative analysis of help-seeking in criminal justice-supervised populations must foreground relational and contextual data to produce actionable findings.

For teams designing research or services, prioritize eliciting social networks, use reproducible framework coding, and test multi-level interventions that target network norms and resource flows.

To move from insight to implementation, combine rigorous qualitative workflows with AI-assisted transcription, thematic synthesis, and cross-segment analysis. Try Evidano for free to pilot these workflows in your next study: Try Evidano for free.