Research protocols pack usable signals, if you can extract them fast. This post shows how to run a pragmatic qualitative analysis of SBSTs (school-based support teams) using the Gauteng protocol (published 16 July 2026) as an example, with a concise methods summary, a 7-step research workflow, and concrete mapping of questionnaires, interviews, and Colaizzi analysis into reproducible outputs. See the original study protocol at PLOS One.
Key Takeaways
This protocol, published 16 July 2026 in PLOS One, defines a sequential explanatory mixed-methods study to develop and test a quality-improvement intervention for SBSTs in four Gauteng secondary schools. Use prevalence data, descriptive phenomenology interviews, and Colaizzi’s seven-step analysis to build a context-aware SBST intervention and a measurable Phase‑2 pilot.
- The protocol samples an estimated population of 6, 042 learners across four schools, with a calculated survey sample of approximately 440 and a Phase‑2 pre-post pilot target of 206 learners (recruitment Jun 2025–Mar 2026; published 16 Jul 2026).
- Colaizzi qualitative analysis is paired with NVivo 15 for coding and traceability: significant statements → formulated meanings → emergent themes, with purposive interviews of about 20 SBST members.
- Use a 7-step workflow (ingest, transcribe, pre-code, analyze by segment, extract quotes, report, iterate) to produce reproducible outputs and stakeholder-ready visuals while enforcing secure storage and PII redaction.
Fast take: what the protocol is and why it matters
The protocol, published 16 July 2026 in PLOS One, defines a sequential explanatory mixed-methods study to develop and test a quality-improvement intervention for SBSTs in four Gauteng secondary schools. Key design choices, a survey sample calculated at approximately 440 from an estimated population of 6, 042, purposive qualitative interviews with about 20 SBST members, NVivo-aided Colaizzi analysis, and a pre-post pilot of roughly 206 learners make this a compact, school-focused implementation study.
- Original source: PLOS One
- Published: 16 July 2026; Recruitment window: June 2025–March 2026; Estimated completion: Dec 2026
- Primary instruments: PHQ-A, GAD-7, ITQ-CA, PEARLS; qualitative method: descriptive phenomenology and Colaizzi’s 7-step
- Why analysts care: the protocol combines survey prevalence, scoping-review evidence, and lived-experience interviews to design a context-aware SBST intervention
Findings snapshot
| Metric | Value | Note / Source |
|---|---|---|
| Adolescent prevalence (global) | ~1 in 7 (ages 10–19) | Protocol cites WHO (2025) |
| Estimated learners in 4 schools | 6, 042 | Study sampling frame |
| Survey sample (calculated) | 440 | Slovin’s formula; stratified by grade |
| Qualitative sample | ≈20 SBST members | Purposive, until saturation |
| Pilot pre-post target | 206 learners | Raosoft calculation for Phase 2 |
| Key dates | Recruitment Jun 2025–Mar 2026; Published 16 Jul 2026 | Protocol timeline |
What happened: study design & methods (plain English)
This section explains the study design and methods in plain English: the team uses a sequential explanatory mixed-methods design with four linked components: a cross-sectional survey to estimate prevalence and associated factors using PHQ‑A, GAD‑7, ITQ‑CA, and PEARLS; a scoping review of interventions (2015–2025); descriptive phenomenological interviews with SBST members analysed via Colaizzi’s seven-step method supported by NVivo 15; and a Phase‑2 pre-post pilot to test a co-developed SBST quality-improvement package.
- Sampling: the study uses four purposively selected secondary schools (two urban, two township) with stratified random sampling within grades for the survey.
- Quantitative analysis: the protocol plans SPSS v30 with descriptive statistics, multivariable regression, ANOVA, and cluster-aware models.
- Qualitative analysis: the protocol applies Colaizzi’s framework with NVivo 15 for coding, triangulation, and extraction of lived-experience themes.
- Ethics: the study obtained SMUREC clearance, uses parental consent and adolescent assent, and provides on-site mental-health support during data collection.
So what for researchers and school teams: qualitative analysis of SBSTs
For qualitative researchers
For qualitative researchers, this protocol models mixed-methods grounding by using prevalence data to prioritise themes for in-depth interviews and feeding qualitative themes into intervention design. Document codebook decisions (NVivo nodes linked to Colaizzi steps) and archive memos for reproducibility.
Tip: treat the scoping review as a code-source, import extracted intervention features as nodes so you can compare what is in the literature versus what SBSTs report.
For school mental-health teams (SBSTs and principals)
For school mental-health teams, the study prioritises feasibility by including two urban and two township schools to ensure operational generalisability. Use the protocol’s screening battery and referral pathway templates as starting points to standardise early identification and on-site support.
Operational move: capture teacher notes and referral forms as structured transcripts (date, grade, symptom flags) to enable trend analysis.
For policy & program evaluators
For policy and program evaluators, the pre-post design with clear instruments (PHQ‑A, GAD‑7, ITQ‑CA) and a planned sample of about 206 yields measurable effect-size expectations. Plan for cluster adjustments and implement routine fidelity checks to separate implementation failure from intervention inefficacy.
Do more, faster with Evidano: map the protocol to an AI-enabled workflow
Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, surveys, and PDFs into a single, auditable project corpus
Evidano ingests protocol inputs including survey spreadsheets, interview audio, scoping‑review PDFs, and administrative notes and normalizes them into a single project corpus ready for coding and cross-analysis.
Relevant Evidano features: survey import, transcript (with custom dictionary and PII redaction), PDF ingestion, and site scraping for grey literature.
Problem: Colaizzi steps are manual → Solution: guided qualitative pipeline
Colaizzi’s seven-step phenomenological method requires repeated extraction, coding, and validation, and Evidano accelerates this with AI-assisted coding suggestions and exportable codebooks aligned to Colaizzi stages.
Relevant Evidano features: hierarchical coding, AI-assisted code suggestions, quote extraction, and participant-level traceability.
Problem: comparing segments (urban vs township) is tedious → Solution: cross-segment analysis
Cross-segment analysis compares theme frequency and co-occurrence between urban and township schools, or between grades and genders, and Evidano can generate ready visuals for stakeholder briefs.
Relevant Evidano features: thematic frequency tables, co-occurrence networks, subgroup filters, and downloadable visuals.
Problem: stakeholder-ready outputs → Solution: reproducible reports & secure data
Reproducible reports with verbatim quotes, visualizations, and methods appendices are suitable for Departments of Education and ethics committees, and Evidano enforces encryption and a strict no-third-party-model-training policy for sensitive adolescent data.
Relevant Evidano features: encrypted storage, audit logs, AI chat over documents, and one-click exports for presentations and manuscripts.
Checklist: 7-step workflow to reproduce this study’s qualitative strand
This checklist is a run-book to convert transcripts and notes into Colaizzi‑aligned themes and stakeholder deliverables.
- 1) Ingest all materials: survey CSVs, interview audio, and scoping-review PDFs into the project corpus.
- 2) Transcribe audio with a custom dictionary (local names, school terms) and enable PII redaction.
- 3) Pre-code using AI-suggested nodes mapped to Colaizzi steps; review and lock the initial codebook.
- 4) Run thematic frequency and co-occurrence analyses by segment (urban vs township, grade, gender).
- 5) Extract representative verbatim quotes and link them to nodes for auditability.
- 6) Produce a stakeholder digest: methods appendix, topline prevalence, qualitative themes, and visual network maps.
- 7) Iterate: after the pilot pre-post, re-run cross-segment comparisons and report changes in effect sizes and themes.
FAQ: qualitative analysis of SBSTs
How do I ensure Colaizzi rigor in software?
Maintain stepwise artifacts and participant validation to ensure Colaizzi rigor in software. Maintain stepwise artifacts (significant statements, formulated meanings, and theme clusters), enable participant review (member‑checking), and export audit logs for transparency.
Can I compare survey scores and themes reliably?
Yes, align respondent identifiers to compare survey scores and themes reliably. Align survey respondent IDs with interview IDs where ethically permissible and use cross-segment and mixed-methods matrices to triangulate quantitative prevalence with qualitative explanations.
Is this approach suitable for sensitive adolescent data?
Yes, the approach is suitable for sensitive adolescent data when secure procedures are followed. Use secure storage, limited access, and PII redaction; the protocol and platform workflows include encryption and data handling practices designed for research-only, non-diagnostic work.
Where can I read the full protocol and instruments?
Read the full protocol and instrument details at PLOS One. See the original study protocol at PLOS One for exact instrument details and timelines.
Wrapping up & next steps
Standardized ingestion, traceable coding, and cross-segment synthesis produce the biggest gains when working from protocols, interviews, or survey exports, which is the capability this Gauteng SBST protocol was designed to enable. Read the full protocol at PLOS One for exact instrument details and timelines.
- Try this on your study by starting a secure trial: Try Evidano for free and import a small corpus (one school’s transcripts and a survey CSV) to evaluate theme extraction and cross-segment visuals.
- Ethics note: this guidance is research-only and non-diagnostic. Always follow local ethics, consent, and child-protection protocols when handling adolescent data.