This post explains the PLOS ONE protocol for studying primary care physicians’ views on large language model (LLM) adoption and shows how AI-enabled qualitative research can accelerate thematic insight. The primary keyword is LLM adoption qualitative research. According to the PLOS ONE protocol published on August 7, 2026, the study uses semi-structured interviews and the newly described Technology Adoption Behavior (TAB) framework to capture physicians’ perceived usefulness, perceived ease of use, and technology–environment fit in British Columbia, Canada. Read the original protocol at PLOS ONE. This post is written for qualitative researchers, health services teams, and product teams who need reproducible methods and faster synthesis of interview data.
Key Takeaways
The PLOS ONE protocol (published August 7, 2026) specifies a descriptive qualitative study that will use semi-structured interviews and the TAB framework to explore how family physicians perceive LLM adoption in clinical practice, and the protocol aims to inform policy and practice. According to PLOS ONE, the study will purposively sample physicians in British Columbia and use thematic analysis with both deductive and inductive coding.
- The PLOS ONE protocol was published on August 7, 2026 and lists received and accepted dates of July 18, 2025 and July 22, 2026 respectively, establishing the timeline of peer review and revision.
- The study plans recruitment from August 1, 2025 to January 31, 2026, a six-month window described in the protocol, with final analysis expected within a year of data collection starting, per PLOS ONE.
- The protocol defines usage strata for LLM experience as 50+ (frequent), 5–50 (occasional), and fewer than 5 uses (nearly no use) in clinicians’ lifetime in clinical contexts, as specified by Pokharel et al. in PLOS ONE.
- The protocol cites Canada-wide primary care access data showing patients without a family doctor rose from 4.5 million in 2019 to about 6.5 million in 2023, framing why primary care settings matter for LLM adoption, according to PLOS ONE.
What happened and how the study works
The PLOS ONE protocol directly answers who, what, when, and how: it is a qualitative descriptive study to interview family physicians in British Columbia about LLM adoption using the Technology Adoption Behavior (TAB) framework, according to Pokharel et al. (PLOS ONE, published August 7, 2026).
According to PLOS ONE, the study will purposively sample physicians across three LLM-use strata (50+, 5–50, <5 lifetime clinical uses) and vary years in practice to reach analytic sufficiency, with recruitment through physician organizations and snowball sampling.
According to Pokharel et al. in PLOS ONE, interviews will be semi-structured, recorded, transcribed verbatim, deidentified, and analyzed with NVivo using deductive codes derived from TAB plus inductive refinement.
According to the protocol in PLOS ONE, reflexive memos and multidisciplinary team discussions are built into the analysis plan to reduce bias and to iteratively refine themes.
Findings snapshot table
| Date | Metric | Value | Implication |
|---|---|---|---|
| Published Aug 7, 2026 | Article status | PLOS ONE protocol published | Peer-reviewed protocol clarifies methods for subsequent empirical work |
| Received Jul 18, 2025 | Peer-review timeline | Received Jul 18, 2025; accepted Jul 22, 2026 | Protocol underwent about one year between submission and acceptance |
| Aug 1, 2025 – Jan 31, 2026 | Planned recruitment window | 6 months | Allows purposive sampling across experience strata and practice years |
| 2019 → 2023 | Primary care access in Canada | 4.5 million → about 6.5 million without a family doctor | Motivates focus on primary care contexts for LLM deployment |
| LLM lifetime uses | Usage strata | Frequent 50+; Occasional 5–50; Nearly no use <5 | Enables cross-segment comparisons in thematic analysis |
Implications for primary care researchers and qualitative teams
The protocol implies that qualitative teams should combine theory-driven and inductive coding to study emerging technologies, according to PLOS ONE.
- Researchers should preregister a coding framework like TAB and plan a hybrid deductive–inductive approach to preserve comparability while capturing novel themes, as recommended in the PLOS ONE protocol.
- Interview teams should collect clear context on participants’ LLM experience using numeric thresholds (e.g., 50+, 5–50, <5), because Pokharel et al. (PLOS ONE) argue these strata improve analytical comparisons.
- Policy-focused dissemination matters: Pokharel et al. state the study will target peer-reviewed journals, professional organizations, and policymaker briefings to influence safe technology integration.
How Evidano helps with LLM adoption qualitative research
Evidano definition
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano ingests transcripts, supports custom dictionaries, and produces thematic and cross-segment analyses that map directly to frameworks like TAB.
Problem: slow synthesis of many interviews
Solution: Evidano automates transcript ingestion, deidentification, and initial thematic extraction so teams can test TAB-derived deductive codes and surface inductive themes faster; see Evidano features.
This saves weeks of manual coding setup while preserving the ability for human-driven code refinement and reflexive memos.
Problem: tracking experience strata and cross-segment patterns
Solution: Evidano’s cross-segment analysis links participant metadata (for example, the protocol’s 50+/5–50/<5 strata and years of practice) to theme frequency and co-occurrence, helping teams replicate the PLOS ONE plan at scale.
Evidano’s visualization and export features make it straightforward to prepare materials for stakeholder briefings and policy audiences.
Problem: transcription quality and PII
Solution: Evidano offers transcription with custom dictionaries and PII redaction to meet the protocol’s requirement for verbatim transcripts and deidentification, reducing manual cleaning time.
Teams can then import cleaned transcripts directly into Evidano for NVivo-comparable coding with faster iteration.
FAQ: LLM adoption qualitative research
What is the TAB framework and why use it for LLM adoption?
Answer: The TAB framework is a unified technology adoption behavior model that combines perceived usefulness, perceived ease of use, and technology–environment fit and is used in the PLOS ONE protocol to structure both interview design and analysis.
Supporting detail: Pokharel et al. (PLOS ONE, published August 7, 2026) describe TAB as integrating TAM traditions and task-technology fit traditions to explain uptake in clinical contexts.
How many interviews will the study need to reach sufficiency?
Answer: The protocol does not specify a fixed number; it plans purposive sampling until sufficiency is reached, consistent with qualitative practice described in PLOS ONE.
Supporting detail: Pokharel et al. cite Vasileiou et al. and other methodology references to justify achieving analytic depth rather than meeting a numeric quota.
Can teams replicate this PLOS ONE protocol outside British Columbia?
Answer: Yes, the protocol’s TAB framework and hybrid coding plan are portable, though Pokharel et al. note that local context and sampling frames should be adapted.
Supporting detail: The authors explicitly justified focusing on British Columbia because of regional primary care dynamics and access trends reported from 2019 to 2023 in the protocol.
How can AI tools like Evidano preserve qualitative rigor?
Answer: AI-enabled platforms preserve rigor by automating repeatable steps like transcription, code application, and theme frequency counts while leaving interpretation and reflexivity to human teams.
Supporting detail: The PLOS ONE protocol emphasizes reflexive memos and multidisciplinary interpretation; Evidano supports those human processes by removing mechanical bottlenecks and exporting audit-ready outputs.
Conclusion & Next Steps
The PLOS ONE protocol by Pokharel et al. (published August 7, 2026) provides a clear, theory-informed plan to study primary care physicians’ perceptions of LLM adoption using semi-structured interviews and the TAB framework.
Researchers replicating or extending this work should preregister TAB-aligned codes, capture numeric experience strata, and plan reflexive team processes as specified in the protocol.
AI-enabled qualitative tools can accelerate the steps the protocol outlines, from transcription to cross-segment thematic matrices, while preserving human interpretation.
If you want to run TAB-guided interview studies faster and produce reproducible thematic and cross-segment analyses, Try Evidano for free.
Topics
- LLM adoption qualitative research
- qualitative analysis of LLM adoption
- primary care physician perceptions
- technology adoption behavior framework
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 NewsAI-enabled Qualitative Analysis: LLM Adoption in Primary CarePractical guide to qualitative analysis of LLM adoption in primary care using the PLOS ONE protocol (Aug 7, 2026). Methods, numbers, and AI-enabled research tools explained.
- 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.