This article shows practical ways to run a reproducible qualitative analysis of school mental health and to operationalize SBST findings quickly using AI workflows. Researchers and school mental-health teams face two problems: lots of mixed data (surveys, interviews, scoping reviews) and slow manual synthesis. The July 16, 2026 PLOS ONE study protocol on a Gauteng School-Based Support Team (SBST) quality-improvement intervention shows a clear use case: n≈440 survey sample planned, 20 SBST interviews, instruments PHQ-A, GAD-7, ITQ-CA, and PEARLS, and NVIVO 15 for Colaizzi analysis. See the original protocol: PLOS ONE. If you want to test these steps on transcripts, surveys, or scoping-review outputs, Evidano can ingest documents and spreadsheets, generate thematic and cross-segment analyses, and export visuals.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that helps apply the July 16, 2026 Gauteng SBST protocol to produce reproducible qualitative analysis of school mental health. This post explains how the protocol’s mixed-methods design (survey, scoping review, phenomenological interviews) combines with an AI-enabled workflow to speed synthesis and generate stakeholder-ready outputs.
- The Gauteng SBST protocol (received Mar 5, 2026; accepted Jun 30, 2026; published Jul 16, 2026) combines a cross-sectional adolescent survey (13–19), a scoping review (2015–2025), and 20 phenomenological SBST interviews for layered qualitative analysis.
- Planned samples and timelines: four purposive schools (2 urban, 2 township), estimated population 6, 042, survey sample n=440, intervention pre-post target n=206; recruitment Jun 2025–Mar 2026, data collection completed Apr 2026, results expected Jan 2027.
- Analytic tools and frameworks: PHQ-A, GAD-7, ITQ-CA, PEARLS for measurement; SPSS v30 for quantitative models; Colaizzi’s seven-step framework and NVIVO 15 for qualitative analysis, with an AI workflow to accelerate coding and cross-segment linkage.
Fast take: why the Gauteng SBST protocol matters
The Gauteng SBST protocol matters because it sets a clear mixed-methods blueprint for prevalence, contextual evidence, and lived-experience themes. The protocol (received Mar 5, 2026; accepted Jun 30, 2026; published Jul 16, 2026) sets a mixed-methods blueprint: a cross-sectional survey of adolescents (13–19), a scoping review (2015–2025), and 20 phenomenological interviews with SBST members. That combination is ideal for a layered qualitative analysis of school mental health, because it produces: (a) prevalence and predictors from instruments, (b) contextual evidence from literature, and (c) lived-experience themes from SBST interviews. Original protocol: PLOS ONE.
- Use-case: four selected schools (2 urban, 2 township); estimated population 6, 042; survey sample n=440; intervention pre-post target n=206.
- Instruments: PHQ-A, GAD-7, ITQ-CA, PEARLS; analysis: SPSS v30 and Colaizzi framework with NVIVO 15.
- Timeline snapshot: recruitment started June 2025; data collection completed April 2026; study completes Dec 2026 with results expected Jan 2027.
Findings snapshot
| Date / Metric | Value | Source / Note |
|---|---|---|
| Received / Accepted / Published | Mar 5, 2026 / Jun 30, 2026 / Jul 16, 2026 | PLOS ONE protocol (Dyani et al. 2026) |
| Schools / Estimated pop | 4 schools (2 urban, 2 township) / 6, 042 learners | Purposive selection for socioeconomic diversity |
| Survey sample / Instruments | n = 440 planned / PHQ-A, GAD-7, ITQ-CA, PEARLS | Stratified random sampling; validated instruments |
| Qualitative sample / Method | 20 SBST members / Descriptive phenomenology | Colaizzi’s 7-step analysis; NVIVO 15 |
| Intervention evaluation | Pre-post quasi-experimental; target n = 206 | SPSS v30; ANOVA, multivariable models, cluster-robust SEs |
| Timeline highlights | Recruitment Jun 2025–Mar 2026; data collection by Apr 2026; completion Dec 2026 | Results expected Jan 2027 |
What happened, how the protocol structures mixed qualitative evidence
The protocol structures mixed qualitative evidence by layering three evidence streams: quantitative surveys, an evidence synthesis, and in-depth interviews. The protocol layers three evidence streams: quantitative prevalence and associations (surveys), an evidence synthesis (scoping review, 2015–2025), and in-depth interviews with SBST members using a phenomenological design. That sequencing (quantitative to qualitative to intervention design) lets researchers triangulate risk factors and lived experience to build context-sensitive SBST interventions.
- Quantitative stage yields prevalence estimates and correlates using SPSS and multivariable regression; planned sample n=440 derived with Slovin’s formula.
- Scoping review follows Arksey & O’Malley plus PRISMA-ScR to map intervention literature and grey sources.
- Qualitative interviews (n≈20) use Colaizzi’s seven-step analytic framework, supported by NVIVO 15 for coding and data management.
So what for researchers and practitioners: qualitative analysis of school mental health
For UX / program researchers
The section explains why program researchers should map qualitative themes to survey strata. If you collect interviews plus survey items, plan for crosswalks: map code frequencies to instrument scores (for example, GAD-7 levels). The protocol’s mixed sequencing is ideal for this, compare themes from SBST interviews with high-anxiety clusters from the survey to build targeted referral pathways.
For policy / district teams
The section explains why policy and district teams should use purposive sampling to surface inequities. Four purposive school settings (urban versus township) let you surface inequities. Use thematic analysis to prioritise interventions that are feasible within resource constraints and align with ISHP and SIAS policies cited in the protocol.
For qualitative analysts
The section explains how Colaizzi plus NVIVO supports reproducibility in qualitative analysis. Colaizzi’s framework plus NVIVO is a robust setup, but manual coding slows scale. The key reproducibility gain is a shared codebook and transparent linkage between quotes, themes, and quantitative strata (age, grade, school location).
Do more, faster with Evidano (mapped to this SBST use case)
Ingest the mixed inputs
This subsection explains how Evidano ingests diverse inputs for one project. Import survey spreadsheets, interview transcripts, scoping-review PDFs and NVIVO exports into one project; Evidano handles documents and spreadsheets so you can analyse themes and frequencies without switching tools.
Automate Colaizzi-like steps
This subsection explains how Evidano accelerates Colaizzi steps. Evidano includes a thematic engine that extracts significant statements, suggests initial codes, and clusters meanings, accelerating Colaizzi’s steps 1 to 4 while preserving traceability to original quotes for credibility and audit trails.
Link themes to survey strata
This subsection explains how to compare themes across participant segments. Run cross-segment analysis to compare themes by school location (urban versus township), grade, or PHQ-A and GAD-7 score band. Evidano produces frequency tables and co-occurrence networks to spot patterns before you design the SBST intervention.
Create stakeholder-ready outputs
This subsection explains how to produce actionable outputs for school leaders and districts. Export hierarchical code maps, word clouds, and clickable quote reports for principals and district teams. That speeds dissemination required by the protocol to education and health departments.
Security and reproducibility
This subsection explains how the workflow supports data protection and reproducibility. Data is encrypted and not used to train third-party models. Import custom dictionaries for local terms and anonymize personally identifying information during transcription, useful when working with minors and following POPIA-like rules referenced in the protocol.
7-step workflow: from protocol to pilot-ready insight
This section lists a minimal reproducible workflow to move from raw protocol data to prioritized SBST intervention items.
- 1) Collect and upload survey CSVs and transcript audio. (Evidano supports transcription with custom dictionary and PII redaction.)
- 2) Run automated coding pass to surface candidate themes and code frequencies.
- 3) Import validated instrument scores (PHQ-A, GAD-7) and link them to interview IDs for cross-segment analysis.
- 4) Review suggested codes, merge into a living codebook, and lock definitions for reproducibility.
- 5) Generate co-occurrence network and phrase clusters to surface referral-barrier themes (for example, stigma, resource gaps).
- 6) Produce a stakeholder brief with top 5 intervention targets and representative quotes (clickable) for school leaders and departments.
- 7) Export analysis and visuals for monitoring pre-post changes during pilot evaluation.
Ethics note
This ethics note explains the protocol’s protections for participants. This protocol obtains parental consent and assent and arranges on-site mental health support. Analyses are for research purpose and non-diagnostic, ensure any flagged high-risk cases follow clinical referral pathways per the study design.
FAQ: qualitative analysis of school mental health
What is the Gauteng SBST protocol?
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The Gauteng SBST protocol is a PLOS ONE study protocol that specifies a mixed-methods SBST quality-improvement intervention. The protocol was received Mar 5, 2026, accepted Jun 30, 2026, and published Jul 16, 2026 and combines a cross-sectional adolescent survey, a scoping review (2015 to 2025), and phenomenological interviews with SBST members. See the original protocol: PLOS ONE.
How many participants are planned in the study?
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The protocol plans defined quantitative and qualitative samples. The protocol plans a survey sample of n=440, an estimated school population of 6, 042 learners across four schools, qualitative interviews with 20 SBST members, and an intervention pre-post target of n=206.
What instruments and analysis frameworks does the protocol use?
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The protocol uses validated instruments and established analytic tools. The protocol uses PHQ-A, GAD-7, ITQ-CA, and PEARLS for measurement, SPSS v30 for quantitative analysis, and Colaizzi’s seven-step framework with NVIVO 15 for qualitative coding.
How can Evidano support this SBST workflow?
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Evidano ingests mixed inputs and automates coding and cross-segment analysis. Evidano imports survey CSVs, transcripts, scoping-review PDFs and NVIVO exports into one project, runs automated coding passes to surface candidate themes, links themes to instrument scores, and exports stakeholder-ready reports while supporting PII redaction and encryption.
What ethics steps does the protocol include?
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The protocol includes parental consent and on-site support to protect participants. The protocol obtains parental consent and assent, arranges on-site mental health support, and treats analyses as research and non-diagnostic with referral pathways for high-risk cases.
Conclusion, next steps
This conclusion restates how to turn the protocol into pilot-ready insight using reproducible qualitative methods. If you are running a mixed-methods SBST evaluation like the Gauteng protocol, you can shave weeks off synthesis and produce reproducible, auditable themes by combining validated instruments with AI workflows. Start by uploading transcripts and survey exports into a single project and run cross-segment thematic and frequency analyses to prioritise intervention elements.
Ready to pilot the workflow on your data? Try Evidano for free to import transcripts, run thematic and cross-segment analyses, and export stakeholder-ready reports aligned with the PLOS ONE protocol.
Original study protocol: PLOS ONE
Topics
- qualitative analysis school mental health
- SBST Gauteng protocol
- PHQ-A GAD-7
- Colaizzi NVIVO
Keep reading
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- Commentary on NewsQualitative Analysis of School-Based Support TeamsTurn the PLOS One (16 Jul 2026) Gauteng SBST protocol into actionable qualitative insights with AI-enabled transcription, coding, and cross-segment analysis.