This post explains the PLOS ONE qualitative research protocol on primary care physicians and large language model (LLM) adoption, and shows how AI-enabled qualitative analysis can accelerate and deepen that work. The primary keyword for this piece is "qualitative analysis of LLM adoption." According to the PLOS ONE study, the protocol targets family physicians in British Columbia and uses semi-structured interviews guided by the new Technology Adoption Behavior (TAB) framework to map perceptions, barriers, and facilitators (PLOS ONE). Researchers and UX teams can use this summary to plan sampling, coding, and synthesis with AI tools that preserve ethics and interpretive rigor.
Key Takeaways
According to the PLOS ONE study, the protocol will use semi-structured interviews and the Technology Adoption Behavior (TAB) framework to describe how primary care physicians perceive LLM adoption in clinical practice.
- The PLOS ONE protocol was published on August 7, 2026 and reports the study design and planned timeline (Pokharel et al., 2026).
- The PLOS ONE protocol plans purposive sampling with LLM experience strata defined as 50+ uses, 5–50 uses, and fewer than 5 uses to capture diverse physician perspectives.
- The PLOS ONE protocol sets a six-month participant recruitment window from August 1, 2025 to January 31, 2026, with data analysis expected within a year of starting data collection.
- The PLOS ONE protocol notes population context: the number of Canadians without a family doctor rose from 4.5 million in 2019 to about 6.5 million in 2023.
What happened and how the protocol works
Answer: The PLOS ONE protocol defines a qualitative descriptive study using semi-structured interviews and the TAB framework to examine physicians' perceptions of LLMs.
According to the PLOS ONE protocol, the research team will purposively sample practicing family physicians in British Columbia with varied prior LLM exposure (frequent, occasional, nearly none) to reach conceptual sufficiency (PLOS ONE).
According to the PLOS ONE protocol, interviews will be recorded, transcribed verbatim, deidentified, and analyzed with a combined deductive and inductive thematic approach using NVivo (PLOS ONE).
According to the PLOS ONE protocol, the TAB framework integrates perceived ease of use and perceived usefulness with task-technology fit to guide both question design and deductive coding (PLOS ONE).
The PLOS ONE protocol acknowledges limits: interviews can reveal internal views but may limit perspective variety, and the study initially reported no collected datasets because recruitment had not yet begun (PLOS ONE).
The PLOS ONE authors write that qualitative description offers a ‘‘summary of events in the everyday terms of those events’’ to prioritize participants’ language and meaning (Pokharel et al., 2026).
Findings snapshot (protocol numeric facts)
| Date / Item | Metric | Value | Implication |
|---|---|---|---|
| August 7, 2026 | Publication date | PLOS ONE protocol published | Protocol publicly available with DOI and methods |
| August 1, 2025 to January 31, 2026 | Planned recruitment window | Six months | Timeline for completing interviews in British Columbia |
| LLM experience strata | Use counts | 50+ / 5–50 / <5 lifetime uses in healthcare context | Ensures sampling across experience levels |
| 2019 vs 2023 | Access context statistic | 4.5 million → about 6.5 million Canadians without a family doctor | Contextualizes primary care capacity pressures |
Implications for qualitative researchers and health technology teams
Answer: The PLOS ONE protocol implies that a theory-informed qualitative design and clear experience strata produce actionable insights for policy and practice.
According to the PLOS ONE protocol, using the TAB framework lets teams connect interview prompts to hypothesized drivers like perceived usefulness, perceived ease of use, and task-technology fit (PLOS ONE).
According to the PLOS ONE protocol, purposive sampling across LLM experience levels and years in practice helps reveal how skill, confidence, and normative pressures shape adoption decisions (PLOS ONE).
According to the PLOS ONE protocol, reflexive memos and multidisciplinary analysis increase interpretive validity by surfacing how researchers' backgrounds influence coding and theme construction (PLOS ONE).
How Evidano helps with qualitative analysis of LLM adoption
Problem: slow synthesis of interview data into policy-ready themes
Answer: Evidano accelerates thematic synthesis while preserving traceability to transcripts.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to the PLOS ONE protocol, teams will transcribe interviews and code deductively and inductively; Evidano supports the same workflow by ingesting transcripts, applying custom codebooks, and producing theme summaries and code frequency matrices.
Evidano integrates transcription with custom dictionaries and PII redaction to match the PLOS ONE protocol's ethics requirement to destroy raw audio and work on deidentified transcripts.
Problem: ensuring reproducible, framework-driven coding (TAB or similar)
Answer: Evidano maps theoretical frameworks to codebooks and runs cross-segment analysis automatically.
According to the PLOS ONE protocol, the TAB framework provides deductive codes to anchor analysis; Evidano lets teams upload a TAB-based codebook and then run both deductive and inductive coding with audit trails.
Evidano's thematic and cross-segment analyses help teams compare LLM experience strata (50+ vs 5–50 vs <5 uses) and produce tables and visualizations for policymaker briefs.
Problem: producing dissemination-ready artifacts for stakeholders
Answer: Evidano exports codebooks, thematic summaries, and visuals suitable for journals and policy briefs.
According to the PLOS ONE protocol, dissemination targets include journals and policymaker briefings; Evidano produces exportable tables, word clouds, co-occurrence networks, and hierarchical code trees to support those outputs.
For more on features, see Evidano features and for secure transcription workflows see Evidano speech-to-text.
FAQ: qualitative analysis of LLM adoption
What sample sizes do researchers plan in the PLOS ONE protocol?
Answer: The PLOS ONE protocol plans purposive sampling to reach conceptual sufficiency rather than a fixed numeric sample.
According to the PLOS ONE protocol, the authors will recruit across LLM experience strata and years in practice and will stop when there is sufficient evidence to answer the research question, citing standards for interview-based sufficiency (PLOS ONE).
How does the PLOS ONE protocol define LLM experience categories?
Answer: The PLOS ONE protocol defines three LLM experience categories: 50+ uses, 5–50 uses, and fewer than 5 uses in a healthcare context.
According to the PLOS ONE protocol, these thresholds are intended to distinguish frequent, occasional, and near-nonusers so that analysis can compare perceptions across real usage patterns (PLOS ONE).
Which qualitative methods and software does the PLOS ONE team plan to use?
Answer: The PLOS ONE protocol specifies semi-structured interviews, reflexive memos, and NVivo for analysis.
According to the PLOS ONE protocol, interviews will be transcribed verbatim, deidentified, and analyzed with a mix of deductive and inductive coding in NVivo, supported by team reflexivity practices (PLOS ONE).
Can AI tools bias qualitative interpretation and how does the protocol address that?
Answer: The PLOS ONE protocol addresses interpretive bias through reflexive memos and multidisciplinary dialogue, not by excluding AI.
According to the PLOS ONE protocol, team reflexivity, monthly codebook review, and senior author oversight are planned to mitigate interpretive bias; researchers should combine AI assistance with human reflexivity to preserve nuance (PLOS ONE).
Conclusion & Next Steps
Answer: The PLOS ONE protocol lays a clear, theory-driven roadmap for qualitative study of primary care physicians' perceptions of LLMs; researchers can pair that approach with AI-enabled workflows to scale synthesis without losing rigor.
According to the PLOS ONE protocol, the study was designed and ethics-approved with recruitment expected between August 1, 2025 and January 31, 2026 and publication on August 7, 2026 (PLOS ONE).
According to the PLOS ONE protocol, the authors recommend reflexive memos, purposive experience strata, and hybrid deductive/inductive coding to surface practical policy and practice insights (PLOS ONE).
If you want to test an AI-enabled qualitative workflow that supports the TAB-style deductive coding and rapid inductive theme discovery, Try Evidano for free.
Topics
- qualitative analysis of LLM adoption
- LLM adoption qualitative research
- physicians perceptions of LLMs
- AI-enabled qualitative analysis
Keep reading
- Commentary on NewsPhysicians and LLMs: Qualitative analysis of LLM adoptionHow primary care physicians view LLMs and how to run qualitative analysis of LLM adoption: methods, timelines, direct quotes, and tools to act with Evidano.
- Commentary on NewsPhysician Views: Qualitative Analysis of LLM AdoptionPLOS ONE 2026 protocol on family physicians' views of LLMs, with practical guidance for AI-enabled qualitative research and tools to speed thematic synthesis.
- Commentary on NewsAdopting LLMs: Qualitative research on physician viewsHow to run LLM adoption qualitative research with physician interviews and the TAB framework, including methods, timelines, and AI-enabled analysis options. Learn practical next steps.