This post refracts a PLOS ONE qualitative study through the lens of AI-enabled qualitative research, with practical notes for researchers and teams doing thematic synthesis. The primary keyword for this post is qualitative analysis Jordan pharmacists. The PLOS ONE study explored community pharmacists' roles in health promotion and disease prevention in Amman, Jordan, using semi-structured interviews and COM-B mapping. The post explains the study's sample, timeline, core findings, and how AI tools can accelerate reliable coding, translation checks, and cross-segment frequency analysis while preserving auditability.
Key Takeaways
According to the PLOS ONE study, 15 community pharmacists in Amman were interviewed and their responses were analyzed by thematic analysis and mapped to the COM-B model (PLOS ONE).
- 15 pharmacists were interviewed between 1 January 2025 and 30 April 2025, with data saturation reached after 14 interviews, according to PLOS ONE (published 5 August 2026).
- 66.7% of participants were female (n = 10) and 73.3% had under 5 years’ experience (n = 11), as reported in PLOS ONE on 5 August 2026.
- Thematic analysis produced four themes (roles, barriers, capacity building, impacts) and deductive mapping found capability, opportunity, and reflective motivation driving pharmacist behaviour, per PLOS ONE (2026).
What happened and how the study worked
What happened: PLOS ONE reports that researchers conducted face-to-face semi-structured interviews with community pharmacists in Amman to explore health promotion and disease prevention activities.
How it was measured: According to PLOS ONE (published 5 August 2026), 15 pharmacists were interviewed between 1 January 2025 and 30 April 2025; interviews averaged 40 minutes and were audio-recorded, transcribed in Arabic, translated to English, and analyzed using Braun and Clarke’s six-step thematic method in NVivo® QSR 14.
Constraints and context: PLOS ONE notes that most participants worked single shifts alone (66.7% were the only pharmacist on duty per shift), most worked in independent pharmacies (80.0%, n = 12), and the sample skewed younger (mean age 27.5 ± 6.1 years), which the authors cite as a limitation for generalizability.
Findings snapshot
| Date / Period | Metric | Value (from PLOS ONE) | Implication |
|---|---|---|---|
| 1 Jan 2025–30 Apr 2025 | Interviews conducted | 15 pharmacists, data saturation after 14 interviews | Small, saturation-driven qualitative sample suitable for thematic depth |
| 5 Aug 2026 (publication) | Female participants | 10 of 15 (66.7%) | Reflexivity: results may reflect early-career, female perspectives |
| Data collection period (2025) | Experience level | 11 of 15 had <5 years experience (73.3%) | Training and undergraduate curriculum gaps likely influential |
| During interviews (reported) | Typical daily workload | ~100 patients/day reported by one participant | Workload pressure cited as a primary barrier to HPDP services |
Implications for qualitative researchers and program designers
What should qualitative researchers take away from the PLOS ONE COM-B mapping?
Answer: The study shows how inductive thematic analysis plus deductive COM-B mapping yields actionable barriers and facilitators that link data to behaviour-change levers.
Supporting detail: PLOS ONE (5 August 2026) used independent double-coding in NVivo and mapped four emergent themes to capability, opportunity, and motivation constructs; researchers can replicate that two-stage pipeline to produce theory-informed recommendations.
What should public health program designers learn for pharmacy-based interventions?
Answer: The study indicates interventions must address staffing, infrastructure, training, and incentive design concurrently.
Supporting detail: PLOS ONE participants reported excessive workload (often one pharmacist per shift, 66.7%), lack of private consultation space, and absence of financial incentives, all dated within the study period (data collected Jan–Apr 2025), so program design that ignores these system constraints will likely fail.
How Evidano helps: AI-enabled solutions for studies like the PLOS ONE COM-B project
Definition: what is Evidano?
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Why that matters: The PLOS ONE team recorded Arabic interviews, translated transcripts, and performed double coding in NVivo; Evidano can accelerate those steps while maintaining audit trails.
Problem: Manual transcription and translation are slow and error-prone → Feature: automated transcription and translation
Answer: The PLOS ONE study transcribed Arabic interviews and translated them to English manually; Evidano offers automated transcription with a custom dictionary and translation with terminology control to match local terms and preserve nuance.
Supporting detail: For a 40-minute interview average reported in PLOS ONE (data collected Jan–Apr 2025), automated, reviewer-verified transcription reduces turnaround time and preserves timestamps for quote-level verification.
Problem: Time-consuming coding and inconsistent codebooks → Feature: AI-assisted thematic and deductive mapping
Answer: The PLOS ONE authors used inductive coding and then mapped themes to COM-B; Evidano replicates that two-stage workflow with AI-suggested codes, co-occurrence networks, and direct mapping to theoretical frameworks.
Supporting detail: Evidano’s thematic, frequency, and cross-segment analyses let teams compare, for example, capability-related quotes versus opportunity-related quotes and export a reproducible COM-B mapping.
Problem: Multi-language validation and auditability → Feature: bilingual transcript checks and encrypted audit trails
Answer: PLOS ONE translated Arabic transcripts into English and checked accuracy; Evidano supports custom dictionaries for translation, PII redaction, and stores encrypted audit logs so reviewers can trace each analytic decision.
Supporting detail: That feature helps meet COREQ-style transparency requirements similar to those PLOS ONE expects for qualitative rigor.
Relevant product links
Explore detailed capabilities on the Evidano features page: Evidano features.
Use Evidano’s speech pipeline for interview work: Evidano speech-to-text.
FAQ: qualitative analysis Jordan pharmacists
How were participants selected in the PLOS ONE study?
Answer: The PLOS ONE study used purposive and snowball sampling to recruit eligible community pharmacists in Amman between 1 January 2025 and 30 April 2025.
Supporting detail: The authors contacted pharmacists via the Jordan Pharmacists Association directory and stopped recruitment after data saturation was confirmed following the 14th interview, with one additional interview to verify saturation, per PLOS ONE (published 5 August 2026).
What does COM-B mapping add to thematic analysis?
Answer: COM-B mapping translates inductive themes into capability, opportunity, and motivation constructs, making qualitative insights actionable for behaviour-change design.
Supporting detail: The PLOS ONE paper mapped four themes to COM-B and concluded that psychological capability, social/physical opportunity, and reflective motivation were primary drivers of pharmacists’ HPDP engagement.
Can AI tools replace human coders for this type of study?
Answer: No, AI tools should augment rather than replace human coders in theory-driven qualitative research.
Supporting detail: PLOS ONE used independent human coders for credibility; Evidano’s AI-assisted coding accelerates candidate code generation and consistency checks but preserves human review and audit trails to maintain trustworthiness.
How would you reproduce the PLOS ONE study faster with AI?
Answer: Use automated transcription, AI-suggested initial codes, bilingual validation, and exportable COM-B mappings to cut manual hours while keeping double-coding and consensus steps.
Supporting detail: For example, for 15 interviews averaging 40 minutes (PLOS ONE, data collected Jan–Apr 2025), automated speech-to-text plus AI-assisted coding can reduce front-end processing by days while preserving the investigator triangulation step the authors used.
Conclusion & Next Steps
The PLOS ONE qualitative study (published 5 August 2026) offers a clear template: inductive thematic analysis followed by deductive COM-B mapping produces theory-linked insights about barriers and enablers for community pharmacists in Jordan.
AI-enabled workflows can reproduce and scale that pipeline while maintaining the credibility practices PLOS ONE required, such as independent coding and translation checks.
If you run qualitative programs and want to speed transcription, preserve bilingual accuracy, and produce reproducible COM-B mappings, consider tools that combine AI suggestions with human oversight.
Get started and test these workflows yourself: Try Evidano for free.
Topics
- qualitative analysis Jordan pharmacists
- COM-B mapping community pharmacists
- AI qualitative research tools
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