Evidano is an AI-powered qualitative data analysis platform that helps teams turn mixed-methods school mental-health studies into actionable recommendations. This post uses the July 16, 2026 PLoS One protocol on a Gauteng SBST (school-based support team) quality-improvement intervention to show how to run a qualitative analysis of school-based mental health and operationalize findings with AI. The protocol combines a cross-sectional survey (n≈440 sample from a population of ≈6, 042 across 4 schools) with ≈20 SBST interviews and a scoping review, and methods include SPSS 30.0.0, NVivo 15, and Colaizzi’s seven-step framework. We map concrete analysis steps and an AI-enabled workflow you can run in Evidano (www.evidano.com) to speed synthesis, compare segments, and generate stakeholder-ready recommendations within weeks rather than months.
Key Takeaways
The PLoS One Gauteng protocol uses a sequential explanatory mixed-methods design to guide a QI intervention for four secondary schools, and Evidano can operationalize the protocol’s qualitative pipeline to accelerate coding, cross-segment comparison, and stakeholder outputs.
- The protocol targets a quantitative sample of n = 440 from an estimated population of ≈6, 042 across four schools, with ≈20 SBST interviews and results expected January 2027.
- Validated instruments include the PHQ-A, GAD-7, ITQ-CA, and PEARLS, with analysis planned in SPSS 30.0.0 and NVivo 15 using Colaizzi’s phenomenological framework.
- Evidano ingests surveys, auto-transcribes audio with PII redaction, supports AI-assisted coding with audit trails compatible with Colaizzi steps, and produces stakeholder-ready visuals and exportable codebooks.
Fast take + source
The Fast take: Dyani et al. published a protocol on 16 July 2026 describing a sequential explanatory mixed-methods QI intervention to strengthen SBSTs in four Gauteng secondary schools.
- Study design: quantitative survey (sample size target 440, population ≈6, 042) then qualitative interviews (≈20 SBST members) then an intervention pilot/pre-post (target n≈206).
- Key tools: PHQ-A, GAD-7, ITQ-CA, PEARLS; analysis with SPSS 30.0.0 and NVivo 15; Colaizzi framework for phenomenology.
- Read the protocol: PLoS One.
Findings snapshot
| Metric | Value | Source / Note |
|---|---|---|
| Protocol published | 16 July 2026 | PLoS One |
| Population (estimated) | ≈6, 042 learners (4 schools) | Protocol sampling frame |
| Quantitative sample | n = 440 (calculated via Slovin's) | Stratified random sampling; grades 8–12 |
| Qualitative sample | ≈20 SBST members | Purposive sampling; data saturation guiding final n |
| Analysis tools | SPSS 30.0.0; NVivo 15; Colaizzi | Mixed-methods pipeline |
| Timeline | Recruitment June 2025–Mar 2026; completion Dec 2026 | Results expected Jan 2027 |
How the study analyzes human experiences (plain English)
The study analyzes human experiences using a sequential explanatory mixed-methods design that first measures prevalence and correlates with validated screens, then explains patterns through in-depth SBST interviews and a scoping review.
- Quantitative signals (for example, elevated PHQ-A or GAD-7 scores by grade or school location) guide purposive sampling for qualitative interviews.
- NVivo 15 will support coding and Colaizzi’s seven-step phenomenological analysis to produce themes grounded in lived experience.
- Triangulation: survey then scoping review then interviews are used to design a context-specific QI intervention and a Phase 2 pre-post pilot uses the same validated instruments to estimate change.
- Statistical safeguards noted in the protocol include mixed-effects models or cluster-robust standard errors, multivariable regression, and ANOVA with p<0.05 plus effect sizes and confidence intervals.
What this means for researchers & school teams: qualitative analysis of school-based mental health
For researchers
Researchers should use the protocol’s sequential design to prioritize where to invest qualitative effort.
Use quantitative cross-tabs (by grade, school type, ACEs) to drive interview sampling and to identify subgroups for purposive recruitment.
Document codebook decisions and intercoder checks, and map Colaizzi’s steps to audit trails and NVivo exports for reproducibility.
For school leaders & policy teams
School leaders should target early wins from segment comparisons such as urban versus township, grade, and gender.
Present segment comparisons as one-page decision memos with verbatim quotes and recommended referral flows, and pilot capacity-building modules for SBST members based on recurring themes like lack of referral pathways and teacher burden.
For program evaluators
Program evaluators should combine pre-post survey effect sizes with thematic change indicators to triangulate impact.
Plan to monitor clustering within schools and adjust analyses for intra-school correlation as described in the protocol.
Do more, faster with Evidano: operationalizing the protocol
Ingest & unify inputs
Evidano ingests and unifies mixed inputs including survey spreadsheets, interview audio, and scoping review PDFs.
Import transcripts and survey sheets, auto-transcribe audio with custom dictionaries for local terms and PII redaction, and import literature PDFs for thematic extraction.
Reproducible qualitative analysis
Evidano supports reproducible qualitative analysis with AI-assisted coding and audit trails compatible with Colaizzi steps.
Import your codebook, accept AI-proposed subcodes, calculate code frequency and co-occurrence, and export an audit trail that documents decisions and supports intercoder checks.
Segment & cross-segment analysis
Evidano enables fast cross-segment comparisons by school location, grade, or ACEs.
Run cross-segment theme frequency and sentiment comparisons in one click to produce tables and visual network graphs for stakeholders.
Stakeholder-ready outputs
Evidano generates stakeholder-ready outputs such as clickable quotes, executive summaries, and slide-ready visuals.
Produce word clouds, co-occurrence networks, and hierarchical code to subcode diagrams to translate themes into recommendations for principals and policy teams.
Security & governance
Evidano provides encryption, retention policy support, and guarantees that user data is not used to train third-party models.
Support for retention policies like 15-year storage plans described in the protocol is available, and Evidano exports maintain an audit trail for decisions.
Quick 7-step workflow to reproduce the protocol’s qualitative analysis in weeks
This 7-step workflow reproduces the protocol’s qualitative analysis in weeks using Evidano and the protocol’s validated instruments.
1. Import surveys (CSV/Excel) into Evidano and tag strata (school, grade, age, ACEs).
2. Auto-transcribe interview audio, apply custom dictionary for local terms and redaction.
3. Run initial thematic extraction, review and refine AI-suggested codes into a codebook.
4. Apply codebook across transcripts and generate code frequency and co-occurrence matrices.
5. Produce cross-segment comparisons (urban vs township, by grade) and extract illustrative quotes.
6. Export NVivo-compatible reports and a one-page policy brief for SBST and Department of Education stakeholders.
7. Iterate after the pilot: update the codebook and run pre-post comparisons to measure change.
Ethics & caution
The protocol and this guidance emphasize that research screening is non-diagnostic and must be paired with clinical pathways and on-site support.
- Ensure parental consent and assent workflows and data minimization for minors as the protocol specifies.
- Keep an audit trail for decisions, and use Evidano exports to document governance and consent procedures.
FAQ: Qualitative analysis of school-based mental health
What research design does the Gauteng SBST protocol use?
The protocol uses a sequential explanatory mixed-methods design that quantifies prevalence then explains patterns with qualitative interviews and a scoping review.
The design begins with a cross-sectional survey followed by purposive interviews with SBST members informed by quantitative signals, and then a Phase 2 pre-post pilot using the same validated instruments.
What sample sizes and timeline does the protocol specify?
The protocol’s quantitative sample target is n = 440 from an estimated population of ≈6, 042 across four schools, with ≈20 SBST interviews and results expected in January 2027.
Recruitment was scheduled June 2025–March 2026 with completion expected December 2026 according to the protocol.
Which validated instruments and analysis tools are used in the protocol?
The protocol uses validated screening instruments PHQ-A, GAD-7, ITQ-CA, and PEARLS and plans analysis in SPSS 30.0.0 and NVivo 15 with Colaizzi’s phenomenological framework.
Statistical approaches include mixed-effects models or cluster-robust standard errors, multivariable regression, and ANOVA with p<0.05 plus effect sizes and confidence intervals.
How can Evidano help operationalize the protocol’s qualitative pipeline?
Evidano can ingest surveys and transcripts, auto-transcribe audio, propose AI-assisted codes, calculate code frequency and co-occurrence, and export audit trails compatible with Colaizzi steps.
Evidano also supports cross-segment comparisons, stakeholder-ready visuals, secure storage, and exportable codebooks for reproducibility.
Wrapping up & next steps
Combining validated quantitative screens with rapid AI-enabled qualitative analysis shortens the policy feedback loop and produces usable recommendations faster for SBST interventions like the Gauteng protocol.
See the PLoS One protocol for full methods: PLoS One.
Ready to try this pipeline on your transcripts and surveys? Try Evidano for free.