Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One article by Connell et al. (published 30 July 2026), help-seeking for mental health and substance use among people in contact with the criminal justice system living in the community is driven by interacting factors labelled desire, ability, and help-seeking context. The primary keyword for this post is qualitative analysis of help-seeking; this post explains the PLOS One findings, gives extractable statistics and quotations, and shows how AI-enabled qualitative research tools can accelerate replication, coding, network-aware synthesis, and multi-level intervention design.
Key Takeaways
The PLOS One study (Connell et al., 2026) shows that social and relational influences within people’s networks powerfully shape help-seeking for mental health and substance use among those under community criminal justice supervision, not just individual attitudes. PLOS One
- In July 2026, Connell et al. published 50 interview-based qualitative analyses conducted between 1 November 2023 and 31 July 2024, confirming the three-part framework: desire to seek help, ability to seek help, and help-seeking context.
- According to background literature cited in the PLOS One paper (Connell et al., 2026), prevalence estimates reported in other studies (as of 2024–2025) include over 40% meeting diagnostic criteria for a substance use disorder, more than 10% for a major depressive episode or psychotic disorder, and 75% for any personality disorder.
- The PLOS One team reports that network-level factors such as shared mistrust of services, normative pressure to appear strong, and dense closed networks repeatedly undermined help-seeking across both a rural LA1 and an urban, deprived LA2 (study published 30 July 2026).
What happened: qualitative analysis of help-seeking in the PLOS One study
The PLOS One study directly examined who, what, when, and how help-seeking influences operate by interviewing 50 adults in contact with the criminal justice system and applying framework analysis to their verbatim transcripts (Connell et al., 2026).
Connell et al. interviewed 50 participants across two Scottish local authority areas between 1 November 2023 and 31 July 2024, then coded transcripts to an a priori framework of desire, ability, and context using Nvivo and team double-coding (PLOS One, 30 July 2026).
The study documents participants' words: for example 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 and miss what you are going to do, " illustrating relational and contextual pressure (Connell et al., PLOS One, 2026).
Findings snapshot
| Date / Period | Metric | Value | Implication |
|---|---|---|---|
| 1 Nov 2023–31 Jul 2024 | Interviews conducted | 50 participants (23 LA1, 27 LA2) | Sufficient qualitative sample for framework analysis and cross-context comparison |
| 30 Jul 2026 | Publication | PLOS One article (Connell et al., 2026) | Peer-reviewed dissemination of framework and quotations for replication |
| Background literature (as cited in PLOS One) | Prevalence estimates | Over 40% SUD, >10% major depression/psychosis, 75% personality disorder | Highlights high clinical need in probation/community-supervised populations |
Implications for researchers and service designers
The PLOS One framework implies that researchers should treat help-seeking as a social process, not only an individual choice (Connell et al., 2026).
- For qualitative researchers: collect network data in interviews and code both individual beliefs and relational dynamics, because Connell et al. found relational subthemes embedded inside both desire and ability.
- For implementation teams: design multi-level interventions (individual, network, community) because the PLOS One study shows that network mistrust and community stigma can block individual readiness.
- For evaluators: measure contextual variables (e.g., service availability, anonymity concerns in small towns) as Connell et al. report context-specific barriers across rural LA1 and urban LA2.
How Evidano Helps: speeding reproducible, network-aware qualitative analysis
Problem: long, manual coding of interviews with network detail
Solution: Evidano automates transcript ingestion and thematic coding while preserving verbatim quotations, enabling faster framework analysis with consistent code application.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. For projects like Connell et al. where relational sub-themes are critical, Evidano can extract network mentions, tag ties, and display co-occurrence networks so teams can see which network-level terms correlate with help-seeking outcomes.
Learn more about relevant features on the Evidano features page: Evidano features.
Problem: preserving participant voice while scaling synthesis
Solution: Evidano preserves pseudonymised verbatim quotes and links them to codes and network positions so analysts can quote participants like P12 or P13 while tracking frequency and cross-segment patterns.
Evidano supports secure transcript import, PII redaction, and exportable matrices that mirror the team matrix Connell et al. created, reducing replication time from weeks to days.
Problem: comparing contexts (rural vs urban) and subgroups
Solution: Evidano’s cross-segment analysis and visualization (co-occurrence networks, hierarchical codes→subcodes) let teams test which themes differ across locations or demographic subgroups, matching the PLOS One approach of LA1 vs LA2 comparisons.
Evidano’s encrypted data model also supports safe sharing for LEAP and advisory panels while maintaining control over sensitive qualitative datasets; see our data security page: Evidano data security.
FAQ: qualitative analysis of help-seeking
How did Connell et al. define the main influences on help-seeking?
Answer: Connell et al. defined three main influences: desire to seek help, ability to seek help, and help-seeking context (PLOS One, 30 July 2026).
Supporting detail: The authors derived these categories from existing help-seeking and healthcare access models and refined them through framework analysis of 50 interviews collected between 1 November 2023 and 31 July 2024.
What role did social networks play in the PLOS One findings?
Answer: Social networks shaped both desire and ability to seek help by transmitting norms, resources, and mistrust, according to Connell et al. (PLOS One, 2026).
Supporting detail: The study reports dense networks normalised substance use, shared epistemic mistrust of services, and either undermined or supported help-seeking via direct encouragement or sabotage.
Can AI tools reproduce the PLOS One framework reliably?
Answer: Yes, AI-enabled qualitative platforms can reproduce and scale framework analysis if trained on carefully coded examples and validated by human coders, as recommended by methods literature and shown practical by tools like Evidano.
Supporting detail: Connell et al. used double-coding and iterative framework refinement; AI workflows should mirror that human-in-the-loop process to maintain trustworthiness and verbatim linkage to quotations.
Is the PLOS One dataset available for secondary analysis?
Answer: Yes, pseudonymised transcripts and quantitative data are deposited at the UK Data Service under accession 10.5255/UKDA-SN-858261, but access requires approval from a data access committee (Connell et al., PLOS One, 30 July 2026).
Supporting detail: The authors note that 49 of 50 participants consented to use of pseudonymised verbatim quotations and that ethical approvals guide access procedures.
Conclusion & Next Steps
The PLOS One study (Connell et al., 2026) shows that qualitative analysis of help-seeking must integrate network, contextual, and individual coding to design effective multi-level interventions.
For researchers and teams planning replication or intervention design, extract the Connell et al. framework, capture network ties in interview instruments, and use tools that link codes to verbatim quotes and visual network outputs.
If you want to accelerate trustworthy, reproducible qualitative synthesis that preserves participant voice and supports network-aware analysis, Try Evidano for free.