Qualitative Analysis of Internationally Educated Nurses

This post explains what a new PLoS One qualitative study found about internationally educated nurses and how AI-enabled qualitative research can make those findings actionable for researchers and healthcare leaders. The primary keyword for this post is qualitative analysis internationally educated nurses. The PLoS One study (Ryan et al., 2026) used four focus groups with 21 participants and a reflexive thematic analysis to trace ecosystem-level barriers to integration and retention; reading those results with AI tools can speed coding, surface cross-level patterns, and produce segment comparisons required by policymakers.

Key Takeaways

According to the PLoS One study, "Building sustainable talent pipelines" (published August 20, 2026), ecosystem forces from national policy to daily ward leadership shape whether internationally educated nurses (IENs) stay or leave PLOS One.

PLOS One reports that the study collected data from four focus groups totaling N = 21 IENs between June 8 and June 29, 2023, and produced 206 initial analytic codes in NVivo™ (reported in 2026).
PLOS One cites OECD figures showing 51.8% of Ireland’s nursing population were IENs in 2023, and the study notes 78% of new nursing registrants in Ireland in 2023 were IENs, highlighting reliance on migrant nurses.
PLOS One participants identified macrosystem barriers such as family visa delays and childcare access; 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" (participant quoted in PLOS One, Ryan et al., 2026).
PLOS One documents exosystem problems (rushed inductions, role mismatch) and microsystem problems (managerial absence, microaggressions) that together erode retention and career progression.

What happened and how the study worked

Answer: The PLoS One study used semi-structured focus groups and reflexive thematic analysis to map how macro, exo, meso and microsystem factors affect IEN talent management.

According to PLoS One, the researchers ran four face-to-face focus groups across one Irish hospital group with 21 IENs from countries including India, the Philippines, Poland, Croatia, Latvia, Zimbabwe and Nigeria, conducted between June 8 and June 29, 2023.

According to PLoS One, all focus groups were audio-recorded, professionally transcribed, and managed in NVivo™ version 14; the analytic team reported generating 206 initial codes and then clustering codes into themes using Braun and Clarke’s reflexive thematic analysis approach.

According to PLoS One, the study organized findings by Bronfenbrenner’s ecological layers: macrosystem (national policy, housing, visas), exosystem (recruitment, induction, credentialing), mesosystem (staffing, leave policy), and microsystem (managerial behaviour and peer support).

Findings snapshot

Date / Source	Metric	Value	Implication
June 8–29, 2023 (PLOS One data collection)	Focus groups	4 groups, N = 21 participants	Qualitative depth, limited generalizability beyond one hospital group
2023 (cited in PLOS One from OECD)	Share of IENs in Irish nursing population	51.8%	High reliance on internationally educated nurses for workforce supply
2023 (cited in PLOS One)	New nursing registrants in Ireland who were IENs	78%	Recruitment focus must be matched by retention interventions
2026 (PLOS One publication)	Initial analytic codes generated	206 codes	Complex, multi-theme dataset where AI-assisted coding can help structure patterns

Implications for healthcare researchers and HR leaders

Answer: The PLoS One findings imply that researchers and HR leaders must design multisystem retention strategies that address visa, housing, induction, career progression and day-to-day leadership.

According to PLoS One, macrosystem fixes (visa family reunification, childcare access, housing policy) are necessary because participants linked family visa delays and childcare shortages directly to attrition decisions.

According to PLoS One, exosystem interventions matter: the study documents rushed or delayed induction ("I got it after maybe six or seven months after arriving" quoted in PLOS One), role mismatch, and unclear promotion pathways, suggesting HR must audit recruitment promises against actual role placements.

According to PLoS One, mesosystem and microsystem changes (consistent leave policy, visible managers, anti-bias promotion practices) are high-return because participants repeatedly identified managerial behaviours and staffing ratios as triggers for intention to leave.

How Evidano helps: from problem to AI-enabled solution

Problem: large qualitative datasets are slow to code and compare across ecosystem layers

Answer: Evidano automates thematic synthesis while preserving researcher-led reflexivity.

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

Evidano maps codes to custom frameworks (for example, macro/exo/meso/micro) and produces frequency, co-occurrence, and cross-segment analyses so teams can quickly see which policy-level issues co-occur with microsystem problems; see Evidano features.

Problem: transcription and PII risk when outsourcing audio

Answer: Evidano offers secure transcription with custom dictionaries and PII redaction.

According to the PLoS One methods, focus groups were professionally transcribed and rechecked against recordings; Evidano’s transcription pipeline mirrors that process while adding role-based access and PII redaction to meet ethics needs; see Evidano speech-to-text.

Problem: detecting cross-level patterns like how visa stress links to ward-level burnout

Answer: Evidano’s cross-segment analyses and co-occurrence networks extract and visualise patterns across themes and participant attributes.

Evidano can ingest transcripts and produce thematic maps similar to the NVivo heuristics described in PLoS One, then let teams filter by country of origin, department, or tenure to test hypotheses such as whether family visa delay quotes cluster with microsystem reports of managerial absence.

Problem: turning findings into actionable policy recommendations quickly

Answer: Evidano generates exportable evidence tables, code hierarchies, and illustrative anonymised quotes for reports and policy briefs.

Evidano helps research teams go from raw transcripts to a policy-ready action grid (macrosystem to microsystem) in a fraction of the time required for manual synthesis, enabling timely engagement with policymakers and HR leaders.

FAQ: qualitative analysis internationally educated nurses

What sample size and methods did the PLoS One study use?

Answer: The PLoS One study used four semi-structured focus groups with N = 21 IENs and reflexive thematic analysis managed in NVivo™ (data collected June 8–29, 2023; published August 20, 2026).

The PLoS One authors recorded and professionally transcribed audio, generated 206 initial codes, and then clustered codes into themes across macro, exo, meso and microsystems.

Which ecosystem layers most urgently need intervention for retention?

Answer: According to PLoS One, both macrosystem (visa, childcare, housing) and microsystem (managerial behaviour, exclusion) require urgent coordinated action because both were directly linked to intention to leave.

The PLoS One participants gave concrete examples tying family visa delays and childcare gaps to relocations, and they also described day-to-day exclusion that erodes commitment.

How can AI help me reproduce or extend the PLoS One analysis?

Answer: AI-assisted platforms can speed coding, surface co-occurrence patterns, and produce cross-segment comparisons that extend reflexive thematic analysis.

Specifically, AI can suggest initial code clusters, highlight frequently co-occurring concepts (for example, 'visa' + 'family separation' + 'intent to leave'), and export analytic memos and quote tables for verification by human researchers.

Is the PLoS One dataset generalisable to other countries?

Answer: No, the PLoS One authors caution that their cross-sectional data come from one hospital group and so generalisability is limited.

The PLoS One paper explicitly notes that future studies should sample multiple hospital groups and contexts to increase transferability beyond the Irish setting.

Conclusion & Next Steps

Answer: The PLoS One study (published August 20, 2026) shows that retention of internationally educated nurses depends on coordinated interventions across macro, exo, meso and microsystems, and AI-enabled qualitative analysis can accelerate the translation of those insights into policy and HR action.

According to PLoS One, examples from participants include long family visa waits, rushed inductions, unclear promotion pathways, and daily managerial behaviours that undermine belonging, all of which point to multisystem interventions.

If you are a researcher or HR leader who needs to replicate or extend this kind of ecosystem analysis at scale, AI-assisted platforms reduce manual coding time, produce cross-segment evidence, and generate the tables and anonymised quotes required for policy briefs.

To try an AI-first workflow for qualitative research, Try Evidano for free.

Topics

qualitative analysis internationally educated nurses
IEN retention qualitative
AI qualitative research for nursing

Keep reading

Browse all articles