Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One scoping review (Åhlund et al., 2026) published on August 7, 2026, interprofessional learning (IPL) between nurses and physiotherapists in hospital practice is enabled chiefly by social contact, team climate, and organizational structures. According to the PLOS One scoping review (Åhlund et al., 2026), the authors screened 1, 973 records and included 11 empirical studies in their synthesis. This post explains how AI-enabled qualitative research methods can reproduce, extend, and operationalize the PLOS One findings for hospital researchers and clinical educators.
Key Takeaways
The PLOS One scoping review (Åhlund et al., 2026) found that sustainable interprofessional learning in hospitals is supported by socialization, a positive team climate, and conducive organizational structures, and hindered by siloed education, physical separation, and hierarchical culture. PLOS One
- The PLOS One review screened 1, 973 records and included 11 studies after full-text review in July 2026, see PLOS One (August 7, 2026).
- According to PLOS One (Åhlund et al., 2026), 10 of the 11 included studies used qualitative interviews (91%), field observations appeared in 5 studies (46%), and surveys in 3 studies (27%).
- The studies in the PLOS One review were published between 2009 and 2022, and sample sizes ranged from 18 to 333 participants, according to PLOS One (Åhlund et al., 2026).
- Quote from the authors in PLOS One (Åhlund et al., 2026): "Sustainable IPL requires organizational structures that enable regular interaction among professionals."
What happened: the PLOS One scoping review in brief
Answer: The PLOS One scoping review systematically identified facilitators and barriers to workplace IPL between nurses and physiotherapists in adult hospital settings.
According to PLOS One (Åhlund et al., 2026), the authors searched PubMed, CINAHL, and Scopus from inception to July 2026 and updated the search in July 2026, yielding 2, 206 records before deduplication and 1, 973 records screened after duplicates were removed.
According to PLOS One (Åhlund et al., 2026), after screening 33 full texts and checking reference lists, 11 peer-reviewed empirical studies met inclusion criteria and were charted against themes including socialization, team climate, and organizational structures.
According to PLOS One (Åhlund et al., 2026), geographic distribution included Australia (4/11, 36%), Sweden and Norway (each 2 studies), Canada (2 studies), and Israel (1 study); all included articles were published between 2009 and 2022.
Findings snapshot
| Date / Source | Metric | Value | Implication for qualitative researchers |
|---|---|---|---|
| July 2026, PLOS One | Records screened | 1, 973 | Large search requires reproducible screening and transparent coding; automate where possible |
| August 7, 2026, PLOS One | Studies included | 11 | Small corpus is suited to deep thematic coding plus cross-case synthesis |
| Included studies (2009–2022) | Method mix | 10 interviews (91%), 5 observations (46%), 3 surveys (27%) | Combine interview transcripts with observation notes to triangulate socialization and team-climate themes |
| Sample sizes reported | Range | 18–333 participants | Use AI to compute code frequencies and map co-occurrence across varied sample sizes |
Implications for hospital researchers and educators
Answer: Researchers and clinical educators should prioritize methods and designs that capture social interactions, shared spaces, and organizational processes when studying IPL.
According to PLOS One (Åhlund et al., 2026), facilitators included joint assessments, shadowing, team rounds, and shared physical spaces, which means qualitative projects should include observational data and documentation of meeting frequency.
According to PLOS One (Åhlund et al., 2026), barriers included physical separation and hierarchical cultures, which suggests interview guides should probe perceived access, status dynamics, and continuity of staffing.
According to PLOS One (Åhlund et al., 2026), the review authors warn that IPL findings are often shaped by ward culture and existing practices, so longitudinal or repeated measures designs improve validity.
How Evidano helps: AI-enabled qualitative analysis for IPL research
Problem: small corpus, high context
Answer: Deep synthesis across 11 qualitative studies or a set of interviews demands cross-case thematic mapping and contextual tagging.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Use Evidano to ingest interview transcripts, observation field notes, and meeting minutes, then run thematic coding across documents.
Problem: messy transcription and domain terms
Answer: Clinical conversations include jargon and abbreviations that break naive speech-to-text and search.
Evidano supports transcription with custom dictionaries and PII redaction via its speech-to-text feature, which reduces manual cleaning time and preserves clinical terminology for reliable coding.
Problem: needing triangulation across methods
Answer: Combining interviews, observations, and surveys requires multi-source linking and cross-segment comparison.
Evidano provides thematic, frequency, and cross-segment analyses and visualizations that let you compare codes across roles, years of experience, or wards; learn more on our features page.
Problem: secure, auditable analysis for clinical settings
Answer: Healthcare research requires data protection and traceability.
Evidano encrypts project data and explains that data are never used to train third-party models; see our data security page for compliance details.
FAQ: qualitative analysis of interprofessional learning
How can AI assist thematic analysis of IPL interview transcripts?
Answer: AI accelerates initial code generation, consistency checks, and co-occurrence mapping so researchers can focus on interpretation.
According to PLOS One (Åhlund et al., 2026), IPL themes hinge on interaction and context, so AI-assisted coding should be combined with manual review to preserve nuance and confirm emergent themes.
Which data types best capture facilitators and barriers to IPL?
Answer: A mixed corpus of interview transcripts, observational field notes, and meeting minutes best captures IPL processes.
According to PLOS One (Åhlund et al., 2026), the included studies used interviews (91%), observations (46%), and surveys (27%), indicating that combining methods improves triangulation.
Can AI detect power dynamics or "us versus them" culture in text data?
Answer: AI can surface linguistic markers of status, exclusion, and recurring phrases but human interpretation is required to confirm meaning.
According to PLOS One (Åhlund et al., 2026), authors observed an "us versus them" attitude in some wards; use AI to flag candidate excerpts, then code and contextualize them by hand.
How should researchers report AI-assisted qualitative findings?
Answer: Report which AI tasks were automated, how human validation was applied, and provide codebooks and example excerpts.
Following PLOS One methods (Åhlund et al., 2026) and PRISMA-ScR guidance, describe search dates, inclusion criteria, and how AI-generated codes were reviewed by researchers.
Conclusion & Next Steps
Answer: Combining the PLOS One scoping review findings (Åhlund et al., 2026) with AI-enabled qualitative methods produces faster, more reproducible syntheses of IPL facilitators and barriers in hospital settings.
According to PLOS One (Åhlund et al., 2026), organizational structures, socialization, and team climate matter most for sustainable IPL, so design your data collection to capture those processes and use AI to accelerate coding and cross-segment comparisons.
If you want to pilot AI-assisted thematic analysis on transcripts, observation notes, or mixed-method datasets, start with an Evidano trial to import documents, run thematic tagging, and visualize code co-occurrence; see our features page to learn how.
To get hands-on, Try Evidano for free.
Topics
- qualitative analysis of interprofessional learning
- interprofessional learning qualitative analysis
- AI qualitative research hospital IPL
- nurse physiotherapist IPL analysis
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