Qualitative Analysis: Internationally Educated Nurses

This post explains how researchers and talent managers can use AI-enabled qualitative analysis to reproduce and scale the ecosystem findings from a PLOS ONE study on internationally educated nurses. The primary keyword is qualitative analysis internationally educated nurses. According to PLOS ONE (Ryan, Berkery, Makowski, 2026), 21 IENs took part in four face-to-face focus groups to surface barriers and supports across macro, exo, meso, and microsystem layers; this post shows which qualitative steps produced those findings and how AI tools help accelerate them for policy and workforce decision makers.

Key Takeaways

According to PLOS ONE (Ryan et al., 2026), an ecosystem-framed reflexive thematic analysis of four focus groups with 21 internationally educated nurses (IENs) found that macro-level factors (visa, housing, childcare, pay) and organizational induction and leadership issues together shape integration and retention.

PLOS ONE reported data collected from 21 IENs across four focus groups conducted between June 8 and June 29, 2023, with a mean session length of 56 minutes (PLOS ONE, 2026).
PLOS ONE documented that Ireland had 51.8% of its nursing workforce internationally educated in 2023, and 78% of new nursing registrants in 2023 were IENs (PLOS ONE, citing OECD and NMBI data).
PLOS ONE recorded 206 initial analytic codes using NVivo™ version 14 during the coding phase and linked those codes to themes across macrosystem to microsystem layers (Ryan et al., 2026).
PLOS ONE participants described concrete barriers in their own words, for example, “We have colleagues who have waited for more than a year to just have your family in. That’s a very big challenge” (participant P2, FG2, PLOS ONE, 2026).

What happened and how the study analysed it

Answer: The PLOS ONE study (Ryan et al., 2026) used four semi-structured, face-to-face focus groups with 21 IENs and reflexive thematic analysis to examine how macro, exo, meso and microsystem factors affect talent management.

According to PLOS ONE (Ryan et al., 2026), recruitment used purposive sampling across one Irish hospital group serving 380, 000 people and all focus groups were audio recorded, professionally transcribed, and analysed using NVivo™ version 14.

According to PLOS ONE (Ryan et al., 2026), the team generated 206 initial codes, iteratively clustered codes into candidate themes, applied Braun and Clarke’s six-phase reflexive thematic analysis, and produced an ecosystem-themed mind map linking macro to micro drivers of retention.

Findings Snapshot

Date / Source	Metric	Value	Implication
June 8–29, 2023 (PLOS ONE)	Focus groups	4 groups, N=21 participants	Sufficient information power for reflexive thematic analysis in one hospital group
2023 (PLOS ONE citing OECD & NMBI)	IEN share of nursing workforce in Ireland	51.8%	High national reliance on internationally educated nurses
2023 (PLOS ONE citing NMBI)	New nursing registrants who were IENs	78%	Most new entrants to register were internationally educated
Analysis phase (PLOS ONE)	Initial analytic codes	206 codes	Granular coding supports multi-layer theme construction
Focus group logistics (PLOS ONE)	Mean duration	56 minutes per session	Depth sufficient for rich qualitative extracts
Publication (PLOS ONE)	Published date	20 August 2026	Timely evidence for workforce policy discussions

Implications for talent managers and qualitative researchers

Answer: Talent managers and qualitative researchers should treat IEN integration as a multisystem problem that requires linked policy, induction, and frontline culture interventions, according to PLOS ONE (Ryan et al., 2026).

According to PLOS ONE (Ryan et al., 2026), macro policies such as visa processing and childcare availability directly affected retention decisions: participants recounted long family reunification waits and reported moves to countries with more family-friendly visas.

According to PLOS ONE (Ryan et al., 2026), exosystem and mesosystem failures like rushed induction, role mismatch, unclear promotion pathways, and understaffing drive dissatisfaction even when pay or registration recognition attract IENs.

According to PLOS ONE (Ryan et al., 2026), microsystem supports such as visible, listening leaders and colleague allyship reduced isolation and improved intent to stay, showing where local interventions can produce rapid improvements.

How Evidano helps: translate PLOS ONE methods into scalable practice

Overview

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

PLOS ONE (Ryan et al., 2026) relied on transcription, iterative coding, NVivo™ visual heuristics, and reflexive thematic analysis; Evidano automates and secures comparable steps while preserving researcher control.

Problem: slow transcription and inconsistent transcripts → Solution: fast, accurate transcription

According to PLOS ONE (Ryan et al., 2026), focus groups were professionally transcribed and checked against audio; Evidano offers secure transcription with custom dictionaries and PII redaction to speed that step.

Use Evidano’s transcription to reduce turnaround time for multi-group projects like the PLOS ONE study and to standardise terminology for downstream thematic coding. See the Evidano transcription feature for details: Evidano Speech-to-Text.

Problem: large, iterative code sets (206 codes) → Solution: thematic + hierarchical coding and visualizations

According to PLOS ONE (Ryan et al., 2026), the authors generated 206 initial codes and used NVivo™ cluster maps as heuristics; Evidano provides thematic, hierarchical coding, co-occurrence networks, and exportable code refinement logs to reproduce that workflow at scale.

Evidano’s content and frequency analyses let teams quickly move from hundreds of codes to ecosystem-level themes while keeping full audit trails for reflexivity and reporting. See Evidano Features to learn more.

Problem: cross-layer analysis and subgroup comparisons → Solution: cross-segment and AI chat

According to PLOS ONE (Ryan et al., 2026), cross-level links between macro visa barriers and micro stress emerged during iterative coding; Evidano’s cross-segment analysis and AI chat let researchers ask targeted queries across transcripts and segments to test such cross-level hypotheses quickly.

Use Evidano’s AI chat to generate audit-ready theme summaries, extract verbatim illustrative quotes (with speaker labels), and produce tables analogous to those in PLOS ONE for policy briefings.

FAQ: qualitative analysis internationally educated nurses

How was the sample for the PLOS ONE study chosen?

Answer: PLOS ONE used purposive sampling to recruit 21 IENs across four focus groups in one Irish hospital group (Ryan et al., 2026).

PLOS ONE (Ryan et al., 2026) reports purposive recruitment to ensure participants had direct experience of hospital shift work and to provide information-rich cases for reflexive thematic analysis.

What analytic method produced the ecosystem themes?

Answer: The PLOS ONE team used Braun and Clarke’s reflexive thematic analysis and a hybrid inductive-deductive coding strategy (Ryan et al., 2026).

According to PLOS ONE (Ryan et al., 2026), the team coded inductively into 206 initial codes, clustered codes into candidate themes, and then organised themes deductively to fit Bronfenbrenner’s macro-exo-meso-micro framework.

Which specific metrics from the PLOS ONE study are useful to report in policy briefs?

Answer: Useful metrics include sample size (N=21), number of focus groups (4), session mean length (56 minutes), and 206 initial codes, as reported by PLOS ONE (Ryan et al., 2026).

PLOS ONE (Ryan et al., 2026) also cites national metrics, 51.8% IEN share of the nursing workforce in Ireland in 2023 and 78% of new registrants being IENs in 2023: that are persuasive for policy audiences.

Can AI tools reproduce reflexive researcher judgement?

Answer: AI tools can accelerate coding, summarisation, and cross-segment queries but should be used to augment, not replace, reflexive human interpretation, as emphasized by PLOS ONE (Ryan et al., 2026).

According to PLOS ONE (Ryan et al., 2026), reflexive TA required iterative team debate and memos; researchers should use AI to surface patterns and preserve reflexive logs while retaining final analytic judgement.

Conclusion & Next Steps

The PLOS ONE study (Ryan et al., 2026) shows that integration and retention of internationally educated nurses is shaped by interacting macro to micro systems, with concrete numbers (N=21, 4 focus groups, 206 codes, 51.8% IEN workforce in 2023) that make the case for coordinated policy and organisational action.

Researchers and talent managers can reproduce and scale the study’s workflow with secure AI-enabled tools for transcription, thematic coding, cross-segment analysis, and visualisation to accelerate policy-ready outputs.

To try these steps on your own dataset, explore how automated transcription and AI-assisted thematic analysis reduce time-to-insight: Try Evidano for free.

Topics

qualitative analysis internationally educated nurses
IEN qualitative analysis
ecosystem approach nurses retention
AI qualitative analysis platform
thematic analysis healthcare interviews

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