AI Qualitative Analysis of Help-Seeking in Justice

According to PLOS One (published July 30, 2026), help-seeking for mental health and substance use among people in contact with the criminal justice system is driven by an interaction of three domains: desire, ability, and help-seeking context, all of which are shaped by social networks.

• The PLOS One study (published July 30, 2026) conducted 50 in-depth interviews between November 1, 2023 and July 31, 2024, with 23 participants in LA1 and 27 in LA2.
• Background prevalence cited in the PLOS One article (July 30, 2026) reports over 40% meeting criteria for a substance use disorder, more than 10% for major depressive or psychotic disorders, and 75% for any personality disorder in similar justice-involved samples.
• The PLOS One authors concluded on July 30, 2026 that “help-seeking is not an individual behaviour, but strongly affected by relational influences” and recommended multi-level interventions targeting networks and context.
• Participant testimony in PLOS One (July 30, 2026) included statements such as "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) and "you just kind of can’t get away from the social" (participant P13).

Answer-first: The PLOS One study (published July 30, 2026) used framework analysis of 50 semi-structured interviews collected with participant-aided sociograms to identify influences on help-seeking among justice-involved people living in the community.

The PLOS One research team interviewed 50 participants (23 in a largely rural LA1 and 27 in a deprived urban LA2) between November 1, 2023 and July 31, 2024 and transcribed interviews verbatim for framework analysis.

According to PLOS One (July 30, 2026), the analytic framework used three top-level categories (desire to seek help, ability to seek help, and help-seeking context) with relational subthemes embedded within each category to reflect social network effects.

PLOS One (July 30, 2026) combined qualitative interview data with social network mapping software (Network Canvas) to capture who network members were, relationship length, and perceived support, and analysed data using NVivo-assisted framework coding.

Answer-first: The PLOS One framework (published July 30, 2026) implies that qualitative researchers should code for relational and contextual themes in addition to individual attitudes when studying help-seeking.

Researchers following the PLOS One approach should collect network data alongside narrative interviews, code at multiple levels (individual, tie, network, community), and explicitly report dates, sample sizes, and recruitment contexts as the PLOS One authors did.

Program teams designing interventions should note the PLOS One recommendation (July 30, 2026) for multi-level strategies that target networks (trusted messengers, peer support design), service accessibility (flexible appointments), and community stigma.

Ethics note: If applying this method to mental health or substance use research, teams should follow the participant protections described in PLOS One (July 30, 2026), including pseudonymisation and data access controls; findings are for research and service design, not clinical diagnosis.

AI-enabled qualitative research makes it practical to scale the PLOS One approach (published July 30, 2026) by joining interview text and network data to map desire, ability, and contextual influences on help-seeking.

The PLOS One study (July 30, 2026) shows that relational factors in social networks are central; AI helps surface those relational signals at scale while preserving researcher-led interpretation.

If your team needs to replicate PLOS One-style framework analysis or build multi-level evaluations, Evidano can ingest transcripts and network exports, run thematic and cross-segment analyses, and keep sensitive data secure.

To start testing the approach on your data, Try Evidano for free.