A July 16, 2026 PLOS One study protocol lays out a mixed-methods plan to develop a quality-improvement intervention for School-Based Support Teams (SBSTs) in Gauteng secondary schools. The protocol (PLOS One) specifies approximately 6, 042 eligible learners across 4 schools, a survey sample of 440, 20 SBST interviewees, SPSS v30 for quantitative analysis and NVivo 15 plus Colaizzi’s framework for qualitative coding. If you run qualitative analysis of adolescent mental health, this post gives a reproducible AI-enabled workflow to turn questionnaires, transcriptions and scoping-review hits into thematic, cross-segment insights and how to run it securely in Evidano (www.evidano.com).
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that consolidates survey spreadsheets, interview audio, scoping-review PDFs, and NVivo exports to speed thematic analysis and reproducible reporting for school-based adolescent mental health studies. This post ties that workflow to a 16 July 2026 PLOS One Gauteng SBST protocol and a seven-step reproducible pipeline that preserves ethics, PII redaction and SPSS/NVivo interoperability.
- The PLOS One protocol (Published 16 July 2026) specifies ~6, 042 eligible learners, a survey sample of 440, 20 purposive SBST interviews, and a recruitment window June 2025–Mar 2026.
- Use a seven-step workflow to import data, auto-transcribe and redact, generate and lock a codebook, run cross-segment analysis, synthesise findings, and export reproducible methods and visuals for ethics and publication.
- Evidano supports AI transcription with custom dictionaries, optional PII redaction, reproducible codebooks, NVivo and SPSS export paths, and document chat for draft methods text.
- Pilot the workflow on 10–20 interviews to check code stability before scaling to the full SBST sample.
Study snapshot
| Metric | Value | Source detail | Why it matters |
|---|---|---|---|
| Publication date | 16 July 2026 | PLOS One | Use recent protocol and timeline as analysis anchor |
| Eligible learners (estimate) | 6, 042 | Protocol sample frame | Defines sampling universe for weighting & cluster checks |
| Survey sample | 440 (calculated via Slovin) | Phase 1 | Primary quantitative prevalence baseline |
| SBST interview sample | 20 purposive | Phase 1 qualitative | Depth interviews for lived experience themes |
| Phase 2 target | 206 (Raosoft estimate) | Pre-post pilot | Feasibility/effectiveness follow-up |
| Software named | SPSS v30, NVivo 15 | Protocol methods | Signals mixed-methods analytics needs |
| Timeline (recruitment) | June 2025 – Mar 2026 | Protocol timeline | Practical window for pilot and replication |
What the protocol does (plain English)
The protocol is a sequential explanatory mixed-methods design that runs a cross-sectional survey, a scoping review, in-depth SBST interviews, then a pilot pre-post quality-improvement intervention. The quantitative phase measures prevalence and drivers of depression, anxiety and trauma using validated instruments (PHQ-A, GAD-7, ITQ-CA, PEARLS), the scoping review synthesises 2015–2025 evidence, and the qualitative phase generates themes that directly inform intervention design.
- Quantitative phase, identifies prevalence and associated factors using SPSS v30, multivariable regression, ANOVA and cluster adjustments.
- Scoping review, synthesizes existing SBST and school-based interventions to provide contextual evidence for the pilot.
- Qualitative phase, conducts 20 SBST interviews analysed with Colaizzi’s seven-step framework and NVivo 15 to generate themes that feed intervention design.
So what for researchers and school teams
For mixed-methods researchers
The protocol provides a pragmatic pipeline: broad quantitative baseline followed by targeted qualitative depth to design interventions. Key reproducible pieces include validated instruments (PHQ-A, GAD-7, ITQ-CA), pretesting (n=20) and explicit saturation criteria for interviews.
Use this protocol as a template for combining prevalence estimates with lived-experience themes so that intervention content maps directly to observed needs.
For school mental-health implementers
SBSTs are feasible delivery points but often lack capacity, and the protocol prioritises capacity building, screening/referral pathways and on-site supports that outcomes can measure pre/post with the same validated tools. Practical implication: collect the same baseline measures and pair them with systematic SBST interview notes to drive iterative quality-improvement cycles.
Policy & funders
The study’s timeline and sample framing (four purposive schools, urban versus township) provide a defensible, replicable design for pilot funding and scale decisions. Evidence triangulated across survey, review and interviews strengthens external validity for regional policy recommendations.
Do more, faster with Evidano
Ingest multi-format inputs
Evidano ingests survey spreadsheets, audio interviews, scoping-review PDFs and NVivo exports into one corpus. Upload your CSVs, transcripts and documents so you do not need to juggle multiple tools.
AI transcription & translation
Evidano can auto-transcribe interviews with custom dictionaries (for local school terms, Afrikaans or isiZulu keywords) and apply optional PII redaction, which is useful given parental consent and ethical constraints in school studies.
Thematic, frequency and cross-segment analysis
Evidano runs AI-assisted coding to generate themes mapped to Colaizzi-style outputs, frequency tables for survey items, and cross-segment comparisons (urban versus township, grade, sex). Exportable visuals include word clouds and co-occurrence networks for stakeholder reports.
Reproducible codebooks & AI chat
Evidano lets you import your codebook or suggest an initial code hierarchy, and it keeps a reproducible codebook. Use the document chat to query quotes, ask for theme summaries, or get draft methods text for your paper or policy brief.
Security & compliance
Data is encrypted, stored privately, and never used to train third-party models, which is important for sensitive adolescent mental health data and ethical approvals. These features complement research ethics but do not replace local IRB approvals.
7-step workflow: reproduce the protocol faster
Follow these seven steps to run a mixed-methods study like the Gauteng SBST protocol with AI support.
- 1) Collect and clean: import survey CSVs (PHQ-A, GAD-7, ITQ-CA responses) and upload audio interviews and PDFs.
- 2) Auto-transcribe and redact: run Evidano transcription with a custom school dictionary and automatic PII redaction for minors.
- 3) Rapid scoping-review triage: cluster and prioritise 2015–2025 hits from your database export by using AI to group titles and abstracts.
- 4) Automated coding pass: generate initial themes from SBST transcripts and review to lock a codebook (Co-coding mode).
- 5) Cross-segment analysis: run frequency and co-occurrence by school type, grade and sex, then export ANOVA-ready tables for SPSS if needed.
- 6) Synthesis: produce a narrative that triangulates survey prevalence, review evidence and SBST themes, include verbatim quotes linked to codes for audit trails.
- 7) Report and iterate: export visuals and a reproducible methods appendix for ethics boards, funders and publications.
Ethics note
The protocol includes parental consent, assent for minors, and on-site mental health supports, and any AI processing of sensitive interviews requires explicit consent and encrypted storage. Evidano supports PII redaction and private data retention policies, and these features complement research ethics but do not replace local IRB approvals.
- Research-only, outputs are for analysis and program design, not clinical diagnosis.
FAQ: qualitative analysis of adolescent mental health
How do I compare themes across schools?
Compare themes across schools by applying one consistent codebook across transcripts and using cross-segment analysis to generate code frequencies and quote distributions. Use Evidano cross-segment analysis to export tables for mixed-effects models in SPSS.
Can AI preserve verbatim quotes for credibility?
AI can preserve verbatim quotes when you retain original transcript segments linked to codes, and you should keep provenance metadata for trustworthiness. Use Evidano’s quote-linking to show provenance in reports for credibility checks.
How should I handle multilingual interviews?
Handle multilingual interviews by transcribing in the original language, applying translation with a custom dictionary, and retaining both versions for audit. Evidano supports custom dictionaries to preserve local terms and stores original and translated transcripts.
What are the protocol’s key sample sizes and timeline?
The protocol estimates about 6, 042 eligible learners across four schools, sets a survey sample of 440, includes 20 purposive SBST interviews and targets recruitment from June 2025 to March 2026. These metrics provide the sampling universe and a practical window for pilot and replication.
Wrapping up & next moves
Adopt an AI-accelerated pipeline to cut coding time, standardise cross-segment comparisons, and keep a reproducible audit trail for ethics and publication if you are planning a school-based mixed-methods study or running the Gauteng SBST protocol. Try the seven-step workflow on a pilot batch of 10–20 interviews to validate code stability before scaling.
- Try the seven-step workflow on a pilot batch of 10–20 interviews to validate code stability before scaling.
- See the original protocol on PLOS One (Published 16 July 2026).
Ready to operationalise this pipeline? Try Evidano for free.