Evidano home
Commentary on News

Qualitative Analysis of School-Based Support Teams

Evidano8 min read

A new PLOS One study protocol (published 16 July 2026) proposes a mixed-methods quality-improvement intervention for School-Based Support Teams (SBSTs) in Gauteng secondary schools. For researchers and program teams doing qualitative analysis of school-based support teams, the payoff is clear: learn how to turn PHQ‑A/GAD‑7/ITQ‑CA interview transcripts and survey data into prioritized, actionable themes that directly shape interventions. This post refracts the protocol through AI-enabled qualitative research workflows and shows concrete ways to reproduce the study’s coding, triangulation, and cross-segment analysis faster and more reproducibly using Evidano (www.evidano.com). You’ll get a quick snapshot of the study inputs, a practical two-week pilot checklist, and specific Evidano features to map to each research step: transcription, translation, codebook, thematic and cross-segment analysis, and visual reports.

Key Takeaways

AI accelerates qualitative analysis of school-based support teams by compressing manual transcription, coding, and triangulation into days while preserving rigor and traceability.

Evidano is an AI-powered qualitative data analysis platform that ingests transcripts and survey CSVs, automates transcription and coding, supports codebook workflows, and links thematic outputs to survey scores.

  • The protocol (PLOS One, 16 July 2026) develops and pilots a SBST quality-improvement intervention for adolescents aged 13–19 across four Gauteng secondary schools using a sequential explanatory mixed-methods design.
  • Quantitative sample is n = 440 (cross-sectional survey) and qualitative interviews are approximately 20 SBST members, with Phase 2 targeting a post sample of ≈206.
  • Evidano maps to the protocol by ingesting CSVs and transcripts, applying PII redaction and custom transcription dictionaries, running auto-coding with codebook import/export, and producing cross-segment frequency and co-occurrence analyses.

Fast take and source

The fast take: Dyani et al. (PLOS One, 16 July 2026) publish a protocol to develop and pilot a SBST quality-improvement intervention for adolescents aged 13–19 in four Gauteng secondary schools.

  • Original protocol: PLOS One
  • Why it matters: School settings are high-impact sites for early mental-health intervention; the protocol combines prevalence measurement, a scoping review, interviews, and a pre-post pilot.

Findings snapshot

ItemValueSource / Note
Publication date16 July 2026PLOS One protocol
Target age13–19 yearsGrades 8–12
Estimated population (4 schools)6, 042 learnersProtocol, recruitment frame
Calculated survey samplen = 440Slovin's formula; stratified sampling
Qual sample (SBST interviews)≈20 members (purposive)Phenomenological interviews; Colaizzi
Quant & qual toolsPHQ‑A, GAD‑7, ITQ‑CA, PEARLS; SPSS v30; NVIVO 15Used for screening, stats, and thematic analysis
Timeline (recruitment/data)Recruitment June 2025–Mar 2026; data complete Apr 2026Protocol timeline

What the study does (methods in plain English)

The study protocol runs two phases: Phase 1 measures prevalence and associated factors via a cross-sectional survey (n=440) and a scoping review, then conducts in-depth interviews with SBST members (~20) using a descriptive phenomenological design and Colaizzi’s seven-step analysis; Phase 2 uses Phase 1 evidence to co-develop, pilot, and evaluate a SBST quality-improvement intervention with a pre-post quasi-experimental design (target post sample ≈206).

  • Sampling: Four purposive schools (2 urban, 2 township), stratified random sampling for learners, purposive sampling for SBST members.
  • Quant analysis: SPSS v30, t-tests, chi-square, multivariable regression, ANOVA, and clustering accounted for with mixed-effects or robust standard errors.
  • Qual analysis: Colaizzi framework supported by NVIVO 15; interviews audio-recorded with consent and transcribed.
  • Ethics: SMUREC approval; parental consent plus adolescent assent; on-site mental health support available.

So what for researchers, UX/education teams and policy analysts

For qualitative researchers

Qualitative researchers should use the protocol as an example of triangulation, where prevalence surveys guide interview sampling and intervention design.

Key reproducible practices in the protocol include validated instruments (PHQ‑A, GAD‑7, ITQ‑CA), pretesting (n=20), independent dual extraction for the scoping review, and transparent codebook development using Colaizzi.

For school/program teams & UX researchers

School and UX researchers should use mixed evidence (survey frequencies plus lived-experience quotes) to prioritize intervention components.

The study’s focus on SBST capacity-building aligns with iterative user-testing: pilot, measure pre-post outcomes, and refine structure and referral workflows.

For policy and monitoring teams

Policy and monitoring teams should adopt the protocol’s measurable endpoints and replicable sampling frame for district-level scale-up decisions.

The protocol provides depression, anxiety, and trauma score endpoints that can be integrated with education and health reporting.

Do more, faster with Evidano (mapping features to the protocol)

Ingest & clean mixed inputs

Evidano ingests CSVs, interview transcripts, PDFs, and scraped web content and applies PII redaction and a custom transcription dictionary to preserve local terms and school names.

The protocol’s problem of handling survey spreadsheets, interview audio, and scoping-review PDFs maps to Evidano’s ingestion and redaction features.

Reproduce Colaizzi + NVIVO workflows

Evidano supports codebook import/export, AI-assisted coding, and hierarchical code visualizations to speed Colaizzi-aligned steps while keeping participant quotes for validation.

The protocol’s manual coding in NVIVO can be accelerated by Evidano’s auto-coding and codebook validation workflow and then validated against transcripts.

Triangulate surveys with themes

Evidano links prevalence outputs to qualitative themes by running cross-segment analysis that shows which themes co-occur with high PHQ‑A and GAD‑7 scores.

The protocol’s manual linking of SPSS outputs to qualitative themes is addressed by Evidano’s frequency tables and co-occurrence networks for stakeholder reports.

Pilot-ready outputs & secure handling

Evidano encrypts data end-to-end, does not use client data to train third-party models, and produces ready-to-share visuals and exportable reports for ethics boards and departments of education.

The protocol’s requirement to handle sensitive transcripts aligns with Evidano’s PII redaction and secure export features.

Start quickly

Evidano lets teams drop transcripts and survey CSVs, map columns (age, grade, school), run auto-theming, validate auto-codes on a 10–20% sample, and run cross-segment analyses to inform intervention design.

The protocol’s practical workflow from transcription to cross-segment analysis can be initiated immediately by following Evidano’s mapping and auto-theming steps.

Two-week pilot checklist: from raw data to intervention-ready themes

The two-week pilot checklist shows practical steps to reproduce the protocol’s Phase 1 insights in a compressed pilot.

  • Day 1–2: Ingest survey CSVs and interview audio; run automatic transcription with a custom dictionary and PII redaction.
  • Day 3–4: Auto-code transcripts and run initial theme extraction; import a validated codebook (Colaizzi-aligned) or create one in Evidano.
  • Day 5–7: Link survey variables (PHQ‑A, GAD‑7, ITQ‑CA scores) to respondent IDs and run cross-segment frequency analysis (by school, grade, sex).
  • Day 8–10: Validate auto-codes on a purposive 10–20% sample, refine code hierarchy, and generate a co-occurrence network and top quotes per theme.
  • Day 11–12: Draft intervention priorities mapped to high-frequency themes and high-risk segments, for example high PHQ‑A plus trauma exposure.
  • Day 13–14: Export visuals and an executive brief for SBSTs and district stakeholders and set follow-up metrics for the pre-post pilot.

Ethics note

The protocol involves sensitive adolescent mental-health data and analyses are intended for research and program design only, not for diagnosis.

The protocol requires SMUREC approval, parental consent and adolescent assent, on-site mental-health support, anonymized outputs, and secure handling of transcripts; Evidano’s PII redaction and encryption align with these safeguards.

FAQ: Qualitative analysis of school-based support teams

What does the PLOS One protocol study?

The protocol develops and pilots a SBST quality-improvement intervention for adolescents aged 13–19 in four Gauteng secondary schools.

The protocol uses a sequential explanatory mixed-methods design with a quantitative cross-sectional survey (n = 440) and qualitative interviews with approximately 20 SBST members, followed by a pre-post pilot targeting a post sample of ≈206.

The primary source is PLOS One.

Which methods and instruments are used in the protocol?

The protocol uses validated screening instruments and a combination of quantitative and qualitative methods.

The instruments listed are PHQ‑A, GAD‑7, ITQ‑CA, and PEARLS for screening, SPSS v30 for quantitative analysis, NVIVO 15 for qualitative coding, and Colaizzi’s seven-step analysis for phenomenological interviews.

How can Evidano speed the study workflow?

Evidano automates transcription, auto-coding, codebook management, and cross-segment analysis to compress manual workflows into days.

Evidano ingests CSVs and transcripts, applies PII redaction and custom dictionaries, runs AI-assisted coding with codebook import/export, and produces frequency and co-occurrence analyses linking themes to PHQ‑A and GAD‑7 scores.

What ethical safeguards are required for this research?

Ethical safeguards include SMUREC approval, parental consent, adolescent assent, anonymized outputs, and on-site mental-health support.

The protocol specifies that analyses are non-diagnostic and that sensitive data must be anonymized and securely handled, which is supported by Evidano’s PII redaction and encryption features.

How do I run a compressed two-week pilot based on the protocol?

A compressed two-week pilot follows a structured ingest-to-report flow: ingest, auto-transcribe, auto-code, link survey scores, validate codes on 10–20% of samples, and export visuals and briefs.

The day-by-day checklist in this post mirrors the protocol’s Phase 1 steps and is designed to produce intervention-ready themes and stakeholder-ready reports within 14 days.

Wrapping up & next steps

The PLOS One protocol (16 July 2026) provides a clear mixed-methods blueprint to build and test SBST interventions in Gauteng.

  • If you are preparing a similar study or piloting SBST capacity-building, use AI-enabled workflows to compress weeks of manual synthesis into days while preserving rigor and traceability.
  • Ready to try this workflow on your transcripts and survey data? Try Evidano for free.
  • Primary source for the protocol: PLOS One

Topics

  • school-based support teams qualitative analysis
  • SBST Gauteng protocol
  • PHQ-A GAD-7 ITQ-CA
  • AI qualitative analysis

Keep reading

Browse all articles

Company

HomeContactBlogsGuides

About

FeaturesThematic Analysis SoftwareAI Thematic AnalysisAI Qualitative CodingQuote TraceabilityMultilingual AnalysisCompare QDA ToolsPlansCase StudiesData SecurityHow We Trained Our AI ModelsAffiliate ProgramHow to Cite & Journal AI PoliciesCompare Evidano with Other SolutionsCertified TrainersHuman vs AIEnvironmental Impact

Preview Evidano

View sample AI docx/pdf analysisView sample AI xlsx/csv analysisView sample AI audio transcriptionTry a demo AI survey / interviewPreview AI ChatbotAI Detection

Account

LoginRegister for Free Plan (free forever, no expiry)Book demo

Newsletter

© Evidano. All rights reserved.

Privacy PolicyTerms of Service