Scale Qual Research: adolescent school mental health

This post distils a PLOS One protocol (published 16 July 2026) that uses a sequential explanatory mixed-methods design with a cross-sectional survey (target n = 440) and 20 SBST interviews to co-design and pilot an SBST quality-improvement intervention.

The practical workflow explains transcription to Colaizzi phenomenological analysis, plus cross-segment triangulation by PHQ-A/GAD-7 strata, and gives a 7-step checklist you can run after collecting interviews and surveys.

The protocol’s qualitative pipeline can be accelerated with AI-enabled tools for transcription, consistent coding, and survey-transcript merging while keeping data encrypted and excluded from third-party model training.

• Protocol publication date and reference: PLOS One, published 16 July 2026, protocol available at the PLOS One article link.
• Sample targets and timing: cross-sectional target n = 440 from an estimated sampling frame of 6, 042 learners; qualitative SBST interviews start at 20 initial interviews, saturation-guided; recruitment June 2025 to Mar 2026, study completion Dec 2026, results by Jan 2027.
• Qualitative method and tools: descriptive phenomenology using Colaizzi’s seven-step method, NVIVO 15 for analysis, plus SPSS v30 for quantitative analyses (ANOVA, multivariable regression).

This section explains the protocol’s methods in plain English.

This protocol is a sequential explanatory mixed-methods study: Phase 1 collects quantitative prevalence data using validated screeners (PHQ-A, GAD-7, ITQ-CA, PEARLS), runs a scoping review covering 2015 to 2025 literature, then follows up with 20 purposively sampled SBST interviews analysed via Colaizzi’s seven-step method in NVIVO 15. Phase 2 uses Phase 1 findings to co-design and pilot an SBST quality-improvement intervention with a pre–post evaluation (target post sample approximately 206).

• Key instruments: PHQ-A (depression), GAD-7 (anxiety), ITQ-CA (trauma), PEARLS (ACEs).
• Qualitative design: descriptive phenomenology with a purposive SBST sample (minimum two years’ experience).
• Integration plan: scoping review results, quantitative patterns, and qualitative themes will triangulate to design the intervention.

This checklist explains seven practical steps to run the qualitative analysis after you have interviews and surveys.

• 1) Consolidate raw files: audio, survey CSVs, field notes. Label with non-identifying IDs.
• 2) Transcribe audio with a custom dictionary and redact PII during transcription.
• 3) Upload transcripts and survey CSVs to your analysis workspace and link IDs to segments.
• 4) Run an AI-assisted initial code sweep to extract candidate themes and significant statements.
• 5) Apply Colaizzi’s steps: extract statements, formulate meanings, cluster into themes, validate with participants where possible.
• 6) Cross-segment analysis: compare themes by PHQ-A/GAD-7 strata, school location (urban vs township), and age.
• 7) Export themed quotes, visuals, and a short memo for the SBST co-design workshop.

This section lists immediate next steps to prepare a pilot and practical options to trial an AI-enabled workflow.

If you are preparing to run or replicate a study like the Gauteng SBST protocol, start by organizing your audio, survey CSVs, and consent metadata. Trial a single-school pilot: transcribe five interviews, run an AI-assisted code pass, and generate visuals for your SBST workshop.

• Explore how an AI-enabled workspace imports transcripts and survey sheets, runs thematic and cross-segment analyses, and outputs stakeholder-ready reports at Evidano.
• Protocol reference: PLOS One.
• Try Evidano for free.