Evidano is an AI-powered qualitative data analysis platform that maps study methods to reproducible workflows and accelerates transcription, TDF coding, cross-segment reporting, and QA. Problem: the SOUND‑BITES pilot (published 14 July 2026) embeds hearing checks into Meals on Wheels NSW but relies on mixed qualitative and quantitative data that take months to clean and code. Payoff: this piece shows a repeatable, AI-enabled workflow for qualitative analysis of hearing pilot interviews so researchers, UX teams, and policy analysts can move from audio to action in days, not weeks. We map the study's methods (Arclight otoscopy; Sound Scouts app; n≈60 volunteers, 600 clients/household members; ethics approved 11 Dec 2025; recruitment started 01/03/2026) to concrete workflow steps, from transcription and TDF coding to cross-segment theme frequency and visual reports. Read on to learn the exact imports, auto-coding patterns, QA checks, and deliverables to reproduce SOUND‑BITES qualitative findings fast.
Key Takeaways
This post shows how to map the SOUND‑BITES pilot methods to a reproducible, AI-assisted qualitative workflow that moves from audio to insight in days rather than months.
The study targets 60 volunteers and about 600 clients, uses optional 1:1 audio interviews with expected saturation at 9–17 interviews, and operates under Macquarie University HREC approval (ID: 16818, 11 Dec 2025).
- Standardise metadata and batch audio to ensure consistent transcription settings and faster downstream coding.
- Use the Theoretical Domains Framework (TDF) as the deductive coding frame, with a machine-readable codebook (CSV/JSON) for AI-assisted coding and auditability.
- Plan iterative waves of analysis (for example, 5–7 interviews per wave) and log emergent themes to determine saturation early (expected 9–17 interviews).
Fast take: what the SOUND‑BITES pilot does
The SOUND‑BITES pilot pairs Master of Clinical Audiology students and Meals on Wheels NSW volunteers to deliver otoscopy, tablet hearing screening, and education to older clients at home.
SOUND‑BITES is a 2026 pilot that pairs Master of Clinical Audiology students and Meals on Wheels NSW volunteers to deliver otoscopy (Arclight), tablet hearing screening (Sound Scouts) and education to older clients at home.
- Ethics: Macquarie University HREC (ID: 16818), approved 11 Dec 2025.
- Recruitment timeline: students began 01/03/2026; participant recruitment expected complete 12/10/2026; full data collection by 30/04/2027.
- Key qualitative input: optional post-program audio interviews (telephone/Zoom), concurrent analysis to saturation (expected 9–17 interviews).
Findings snapshot (study facts you’ll use in analysis)
| Metric | Value | Source / Note |
|---|---|---|
| Target volunteers | 60 | Protocol (Meals on Wheels NSW sites) |
| Target clients & household members | 600 | Clients aged ≥50; excludes diagnosed cognitive impairment or regular hearing‑device users |
| Sites | 8 Meals on Wheels NSW locations (metropolitan Sydney) | List in protocol |
| Primary qualitative inputs | Optional 1:1 interviews (clients & volunteers) | Audio recorded; transcribed; coded to Theoretical Domains Framework |
| Key dates | Ethics 11/12/2025; students recruited 01/03/2026; recruitment end 12/10/2026 | From protocol timeline |
How the qualitative arm works (plain English)
The qualitative arm collects optional audio interviews by phone or Zoom, transcribes intelligent verbatim, returns transcripts for participant review, and maps codes to the Theoretical Domains Framework.
Interview data are optional, audio‑recorded, and conducted by phone or Zoom.
Transcripts are produced as intelligent verbatim, manually checked against audio, returned to participants for review, then imported into NVivo for deductive content analysis mapped to the Theoretical Domains Framework (TDF).
Coding involves two coders with a third adjudicator for disagreements; data collection and analysis run concurrently to identify saturation (9–17 interviews).
- Interview content: program value, usability of Sound Scouts/Arclight, volunteer comfort, barriers to help‑seeking.
- Analytic frame: deductive coding into TDF domains for implementation evaluation.
- Outcome: acceptability, feasibility, and preliminary help‑seeking behaviours at 6 months.
Implications for researchers: what to watch for in qualitative analysis of the pilot
Signal vs noise: environment matters
Home visits introduce variable background noise that affects audio quality and automated transcription accuracy, so flag noisy recordings and prioritise higher‑quality transcripts.
Home visits introduce variable background noise that affects audio quality and automated transcription accuracy.
Flag recordings by noise level metadata and prioritise higher‑quality transcripts for thematic extraction.
Document contextual anchors (site, volunteer ID, student vs volunteer speaker) so you can segment themes by delivery model.
Mapping to TDF: keep the codebook portable
The Theoretical Domains Framework is the study's deductive frame, so capture the final codebook as a machine‑readable file for reuse and automated coding.
The protocol uses TDF domains.
Capture the final codebook as a machine‑readable file (CSV/JSON) for reuse and automated coding.
Record coder decisions and edge cases to train AI‑assisted coders and reduce inter‑rater variability.
Data saturation planning
Data saturation is expected between 9 and 17 interviews, so plan iterative analysis waves and log themes after each wave to determine saturation early.
Plan iterative analysis in waves (e.g., 5–7 interviews per wave) and log emergent themes after each wave to determine saturation early.
Track which themes appear across segments (site, age, device status) to prioritise dissemination.
Do more, faster with Evidano (mapped to SOUND‑BITES needs)
Problem: noisy, variable audio → Solution: robust transcription
Evidano is an AI-powered qualitative data analysis platform that auto-transcribes audio with intelligent verbatim, supports a custom dictionary, and offers PII redaction to meet ethics requirements.
Evidano auto‑transcribes audio with intelligent verbatim, supports a custom dictionary (e.g., Sound Scouts, Arclight, site names), and offers PII redaction to meet ethics requirements.
Problem: manual TDF coding is slow → Solution: AI‑assisted deductive coding
Import a machine‑readable TDF codebook and use AI-assisted pre‑coding to reduce manual coding time, then reconcile tags with quick coder workflows and an audit log.
Import the TDF codebook into Evidano, run AI‑assisted coding to pre‑tag transcript segments, then use quick coder reconciliation to reach consensus faster while preserving audit logs.
Problem: comparing sites and subgroups → Solution: cross‑segment analysis
Cross‑segment reports enable frequency tables and subgroup comparisons by site, volunteer, or age to prioritise implementation changes.
Evidano produces thematic frequency tables, cross‑segment comparisons (by site, volunteer, age group), and confidence intervals for binary outcomes to prioritise implementation changes.
Problem: stakeholder buy‑in requires clear evidence → Solution: quotable outputs & visuals
Exportable quote packs and visualisations make it easier to brief Meals on Wheels leadership and health services with actionable evidence.
Generate exportable quote sets, co‑occurrence networks, and hierarchical code→subcode visualizations for briefs to Meals on Wheels leadership and health services.
Security & reproducibility
Evidano uses encrypted storage and proprietary LLMs tuned for qualitative research, and client data is not used to train third‑party models to meet ethical needs.
Evidano uses encrypted storage and proprietary LLMs tuned for qualitative research; client data is not used to train third‑party models, a fit for ethically sensitive health research.
Checklist: from audio to insight (2‑week pilot workflow in Evidano)
This checklist describes a two-week batch workflow to process 10 interviews end-to-end using the steps aligned with the study protocol.
Use this checklist to process a batch of 10 interviews end‑to‑end:
- 1) Ingest audio + metadata (site, volunteer/student, date).
- 2) Auto‑transcribe with custom dictionary and enable PII redaction; do quick manual QC.
- 3) Import existing TDF codebook (CSV) and run AI pre‑coding.
- 4) Two researchers reconcile AI tags inside Evidano; track agreements/disagreements.
- 5) Run thematic frequency and cross‑segment reports (site, age band, referral outcome).
- 6) Produce quote packs and visualizations (co‑occurrence network, hierarchy) for stakeholders.
- 7) Export a decision memo and append raw transcripts/QA logs for the ethics file.
Quick QA & validity tips
Quick QA checks such as sampling AI codes and keeping an audit trail support reproducibility and ethics review.
Small checks that save time:
- Randomly sample 10% of AI codes for manual verification each wave.
- Keep an audit trail linking audio → transcript → code changes for reproducibility and ethics review.
- Log environmental noise metadata and exclude recordings below a set SNR threshold from automated extraction (or treat them as manual jobs).
Wrapping up: next steps for teams analyzing SOUND‑BITES interviews
Teams preparing to analyze SOUND‑BITES interviews should standardise metadata, export the TDF codebook, batch audio for consistent transcription, and use AI to pre‑code so humans can focus on synthesis.
If you’re preparing to analyze SOUND‑BITES‑style interviews, start by standardising metadata, exporting the TDF codebook, and batching audio for consistent transcription settings.
Use AI to pre‑code and surface frequency patterns, then focus human effort on synthesis and interpretation.
To try the exact workflow above and accelerate your qualitative analysis, see Evidano or Try Evidano for free. For the original protocol and full methods, consult the study: PLOS ONE study.
Ethics note: this post focuses on research workflows and does not provide clinical advice. Maintain participant consent, transcript review, and PII safeguards when handling health data.
FAQ: SOUND‑BITES qualitative analysis
What is the SOUND‑BITES pilot and who does it involve?
The SOUND‑BITES pilot pairs Master of Clinical Audiology students and Meals on Wheels NSW volunteers to deliver otoscopy, tablet hearing screening, and education to older clients at home.
The study targets 60 volunteers and about 600 clients/household members across eight Sydney sites and combines baseline and 6‑month surveys with optional 1:1 audio interviews.
How many interviews will the qualitative arm need to reach saturation?
Saturation is expected between 9 and 17 interviews according to the protocol's concurrent analysis plan.
The protocol plans concurrent data collection and analysis to identify saturation (expected 9–17 interviews), using iterative waves for early stopping if themes stabilise.
What coding framework does the study use for deductive analysis?
The study uses the Theoretical Domains Framework (TDF) as its deductive coding frame.
Transcripts are coded into TDF domains, with two coders and a third adjudicator for disagreements, and the final codebook should be captured as a machine‑readable file for reuse.
How should researchers handle noisy home‑visit audio?
Researchers should flag noisy recordings by noise metadata, prioritise higher‑quality transcripts, and exclude or manually process recordings below a set SNR threshold.
Home visits introduce variable background noise that affects automated transcription accuracy, so document contextual anchors and treat low‑SNR files as manual jobs if needed.