AI Methods: Qualitative Analysis of Help-Seeking

According to the PLOS One article by Connell et al. (published July 30, 2026), help-seeking for mental health and substance use among people supervised in the community emerges from interacting individual desire, individual ability, and the relational and contextual help-seeking environment.

• The PLOS One study interviewed 50 people between 1 November 2023 and 31 July 2024 and analysed transcripts with a framework approach, confirming relational influences shape help-seeking (50 interviews, Connell et al., 2026).
• Connell et al. (PLOS One, July 30, 2026) recruited 23 participants in LA1 and 27 participants in LA2, showing contextual differences between a more rural LA1 and a deprived urban LA2.
• The PLOS One article highlights prior prevalence estimates cited in the literature: over 40% meeting criteria for a substance use disorder, more than 10% for major depression or psychosis, and 75% for any personality disorder in comparable justice-involved groups (as cited by Connell et al., 2026).
• The PLOS One authors conclude, in their words, that “help-seeking is not an individual behaviour, but strongly affected by relational influences that operate between individuals, across social networks, and via cultural norms” (Connell et al., 2026).

The PLOS One study collected 50 semi-structured, audio-recorded interviews and used framework analysis to identify themes of desire, ability, and context affecting help-seeking (Connell et al., 2026).

According to Connell et al. (PLOS One, 2026), interviews were conducted from 1 November 2023 to 31 July 2024, transcribed verbatim, pseudo-anonymised, and coded iteratively using Nvivo with independent double-coding and matrix summaries to ensure consistency.

The PLOS One team combined qualitative interviews with participant-aided social network diagrams to foreground relational and network-level influences that are often missed in survey-only designs.

Direct quotations from the study illustrate lived experience: Participant P13 told researchers, "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 and miss what you are going to do" (P13, quoted in Connell et al., PLOS One, 2026).

Multi-level, relational analysis should be the default when studying help-seeking in justice-involved populations, because the PLOS One study shows relational norms and resources alter both motivation and capacity to act (Connell et al., 2026).

• Design: According to Connell et al. (PLOS One, 2026), include participant-aided network mapping alongside interviews to capture shared norms and resource flows; the PLOS One team used Network Canvas for this purpose.
• Sampling: The PLOS One authors recruited across contrasting localities (rural LA1 and urban LA2) and recommend purposive recruitment to surface context-specific barriers; the team planned capacity for 60 interviews and concluded after 50 with data richness achieved.
• Analysis: Connell et al. (PLOS One, 2026) used framework analysis with independent double-coding and matrix summaries; qualitative teams should document iterative codebook changes and cross-check at the case-by-theme matrix level.

The PLOS One study (Connell et al., 2026) demonstrates that qualitative analysis which makes relational context explicit changes how we understand help-seeking for people in contact with the criminal justice system.

AI-enabled qualitative workflows shorten the path from raw transcripts and sociograms to an actionable framework by automating initial coding, linking quotes to network roles, and producing cross-segment frequency reports that researchers can validate.

If you run qualitative studies with sensitive interviews and network data, adopt multi-method designs and use tools that support PII redaction and encrypted storage to meet ethical requirements (the PLOS One team used controlled deposition for pseudonymised data).

To try an AI-enabled qualitative analysis workflow that supports thematic, cross-segment, and network-linked analysis, Try Evidano for free.