Qualitative Analysis of Internationally Educated Nurses

This post interprets the PLOS ONE study through the lens of AI-enabled qualitative research, aimed at HR leaders, nurse researchers, and qualitative teams. The primary keyword "qualitative analysis of internationally educated nurses" appears throughout this guide to help researchers find concrete, extractable insights. According to Ryan, Berkery, and Makowski in PLOS ONE (published August 20, 2026), the study used four face-to-face focus groups with 21 internationally educated nurses (IENs) held between June 8 and June 29, 2023, and analysed transcripts with NVivo and reflexive thematic analysis.

Key Takeaways

The short answer: the PLOS ONE study shows that host-country macro, exo, meso, and microsystem factors together shape integration and retention, and that addressing systems beyond the ward is essential to sustain IEN workforce pipelines. See the original PLOS ONE article.

According to PLOS ONE (published August 20, 2026), the qualitative study interviewed 21 IENs across four focus groups run between June 8 and June 29, 2023.
According to the PLOS ONE authors citing OECD data, in 2023 Ireland had 51.8% of its nursing workforce educated abroad and 78% of 2023 new nursing registrants were IENs, showing heavy reliance on international recruitment.
According to PLOS ONE (2026) participants cited visa delays, childcare shortages, and housing problems as macrosystem barriers; one participant said, "We have colleagues who have waited for more than a year to just have your family in. That’s a very big challenge" (P2, FG2).
According to PLOS ONE (2026) exosystem and mesosystem issues included rushed inductions, role mismatch, and unclear promotion criteria, with one nurse reporting, "I got it after maybe six or seven months after arriving" about induction delays (P3, FG4).

What happened and how the study was done

Answer: Ryan, Berkery, and Makowski used reflexive thematic analysis on four in-person focus groups to map how layered systems affect IEN talent management.

According to the PLOS ONE article (published August 20, 2026), the research used purposive sampling to recruit 21 IENs from Poland, India, Croatia, Latvia, the Philippines, Zimbabwe, and Nigeria, with all groups audio-recorded and professionally transcribed.

According to PLOS ONE (2026), analysis was done with NVivo version 14 and Braun and Clarke’s six-phase reflexive thematic analysis, generating 206 initial codes which were iteratively refined into ecosystem-themed findings.

According to PLOS ONE (2026), the authors framed findings across Bronfenbrenner’s four nested layers: macrosystem, exosystem, mesosystem, and microsystem to highlight cross-level dynamics.

Findings snapshot

Date / Source	Metric	Value / Example	Implication
August 20, 2026 (PLOS ONE)	Sample	21 IENs in 4 focus groups (June 8–29, 2023)	Qualitative depth, limited generalisability beyond the single hospital group
2023 (OECD via PLOS ONE)	IEN share in Ireland	51.8% of nurses educated abroad (2023)	High system reliance on international recruitment; retention critical
2023 (National register data cited in PLOS ONE)	New entrant composition	78% of 2023 new nursing registrants in Ireland were IENs	Recruitment pipelines skewed toward foreign-trained nurses
PLOS ONE focus groups (2026 report)	Common macrosystem issues	Visa delays, childcare access limits, housing shortages	Policies outside hospitals directly affect retention

Implications for HR leaders and nurse researchers

Answer: HR leaders must treat IEN retention as a multi-layer problem that requires policy, organisational, and frontline interventions, not only recruitment.

According to PLOS ONE (2026), macrosystem barriers such as family visa delays and childcare shortages were direct drivers of attrition intentions; one participant said, "that’s a very big challenge" when describing year-long family reunification waits (P2, FG2).

According to PLOS ONE (2026), exosystem issues included recruitment reality gaps and rushed inductions, which the authors linked to increased stress and role mismatch on arrival.

According to PLOS ONE (2026), mesosystem and microsystem problems (understaffing, inconsistent leave, and perceived managerial exclusion) reduced morale and career progression intent, suggesting interventions must include fair promotion pathways and consistent local onboarding.

How Evidano helps: applying AI-enabled qualitative research to IEN workforce studies

Problem: large qualitative transcripts and slow synthesis

Answer: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano accelerates transcript ingestion and coding so teams can move from audio to thematic summaries faster than manual-only workflows.

For example, the PLOS ONE study used NVivo for organisation; teams using Evidano can add AI-assisted thematic extraction, frequency counts, and co-occurrence networks to highlight cross-level patterns faster.

Problem: tracking cross-level themes across small focus groups

Answer: Evidano automates cross-segment and cross-node comparisons so researchers can see which themes show up at macro, exo, meso, and micro levels.

Evidano’s thematic and cross-segment analyses make it practical to detect, for instance, which recruitment promises (exosystem) map to visa stress (macrosystem) and day-to-day burnout (microsystem).

Use Evidano’s features to combine coded extracts, frequency metrics, and visual maps that mirror the ecosystem mind map used in PLOS ONE.

Problem: secure transcription and multi-language datasets

Answer: Evidano offers encrypted transcription and translation with custom dictionaries to preserve technical terms and names.

Evidano’s secure speech-to-text pipeline helps reproduce the PLOS ONE workflow (recording to verbatim transcript) while providing PII redaction and translation for multilingual IEN samples; see speech-to-text.

FAQ: qualitative analysis of internationally educated nurses

What methods did the PLOS ONE study use and why does that matter for my research?

Answer: The PLOS ONE study used semi-structured focus groups and Braun and Clarke’s reflexive thematic analysis to surface lived experience and system-level themes.

According to PLOS ONE (published August 20, 2026), face-to-face focus groups (N = 21) were audio-recorded, professionally transcribed, and analysed with NVivo and reflexive thematic methods to produce depth over breadth.

How transferable are the PLOS ONE findings to other health systems?

Answer: The findings are transferable as hypotheses but not directly generalisable because the sample came from one Irish hospital group, as the authors note in PLOS ONE (2026).

According to PLOS ONE (2026), the authors explicitly caution that single-group, cross-sectional qualitative data limit generalisability and recommend multi-group future studies for broader claims.

How can AI tools preserve rigor when re-analysing focus group transcripts?

Answer: AI tools can enhance rigor by speeding coding, surfacing co-occurrence patterns, and providing reproducible frequency metrics while keeping researcher-led interpretation central.

According to PLOS ONE (2026), the original team used NVivo for organisation but emphasised researcher-led reflexivity; AI-enabled workflows should mirror that approach by combining automated extraction with documented reflexive decisions.

Can Evidano help me reproduce an ecosystem-style thematic map like the PLOS ONE authors?

Answer: Yes, Evidano can generate hierarchical code maps and co-occurrence networks that support ecosystem-style analyses.

Evidano’s visualizations and code→subcode hierarchies let researchers reproduce the multi-layer maps used in PLOS ONE while preserving audit trails for transparency.

Conclusion & Next Steps

Answer: The PLOS ONE study demonstrates that retaining internationally educated nurses requires coordinated policy and organisational action across macro, exo, meso, and microsystems, and AI-enabled qualitative tools can speed insight-to-action.

According to PLOS ONE (published August 20, 2026), addressing visa, childcare, housing, induction, and managerial practice together will better protect the talent pipeline than recruitment alone.

If you run IEN research or HR programmes and want reproducible thematic maps, cross-segment analyses, and secure transcription workflows, explore how Evidano can accelerate your project with integrated AI features and visualizations: Try Evidano for free.

Topics

qualitative analysis of internationally educated nurses
IEN retention qualitative
ecosystem approach to IEN integration
AI qualitative research tools

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