Evidence map›Paper›PMID 42145478›Full record

ArticleFrontiers in public health2026

Exploring the intention to leave the nursing profession: a descriptive qualitative study in Italy.

Martina Saraga, Silvio Quirini, Angela Peghetti, Stefano Durante, Manuela De Rosa, Angela Vetromile, Lucia Golfieri, Andrea Turolla, Stefano Benini

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Martina SaragaTranslational Rehabilitation Sciences Group, Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bologna, Bologna, Italy.
Silvio QuiriniProfessional Development and Implementation of Research in Health Professions Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Angela PeghettiProfessional Development and Implementation of Research in Health Professions Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Stefano DuranteProfessional Development and Implementation of Research in Health Professions Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Manuela De RosaProfessional Development and Implementation of Research in Health Professions Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Angela VetromileCardio-Thoracic and Vascular Anesthesia and Intensive Care Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Lucia GolfieriHealth Professions Direction, IRCCS Azienda Ospedaliero-Universitaria Bologna, Bologna, Italy.
Andrea TurollaTranslational Rehabilitation Sciences Group, Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bologna, Bologna, Italy.
Stefano BeniniAUSL di Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Nurses and nursing assistants working in high-intensity hospital settings are exposed to demanding workloads, organizational pressure, and substantial emotional strain, factors commonly associated with job dissatisfaction and an increased intention to leave the profession. Intention to leave is a recognized predictor of actual turnover and is particularly relevant in high-intensity cardio-thoracic-vascular units, where care complexity and emotional burden are especially pronounced. This study aimed to explore the experiences, perceptions, and motivations of nurses and nursing assistants working in a high-intensity cardio-thoracic-vascular setting with regard to their intention to leave. Methods: A descriptive qualitative study was conducted at an Italian university hospital in accordance with the Consolidated Criteria for Reporting Qualitative Research. Four focus groups were carried out between May and June 2025 and involved nurses and nursing assistants employed in a high-intensity cardio-thoracic-vascular unit. All discussions were audio-recorded, transcribed verbatim, and analyzed using inductive thematic analysis. Methodological rigor was ensured through researcher triangulation and participant validation. Results: Five main themes emerged from the analysis: (1) motivation and professional identity, (2) working in high-intensity care, (3) interprofessional relationships, (4) symbolic representation of professional roles, and (5) intention to leave and job dissatisfaction. Participants described emotional exhaustion, workload imbalance, and a perceived lack of professional recognition as primary drivers of intention to leave. Conversely, strong teamwork, supportive peer relationships, and empathetic leadership were identified as protective factors. Discussion: Intention to leave among nurses and nursing assistants arises from the interaction of emotional, relational, and organizational dimensions. These findings highlight the need for interventions focused on supportive leadership, structured communication, and staff wellbeing initiatives to strengthen motivation, reduce attrition, and enhance the sustainability and quality of care in high-intensity clinical settings. Conclusion: These findings deepen understanding of intention to leave in high-intensity care and may inform future retention strategies.

Indexed as

IntentionJob SatisfactionNursing AssistantsNursing Staff, HospitalPersonnel TurnoverAdultAttitude of Health PersonnelEmotional ExhaustionFemaleFocus GroupsHumansItalyMaleMiddle AgedMotivationQualitative Researchhigh-intensity careintention to leavenursingorganizational wellbeingqualitative research

Identifiers

PMID42145478
PMCPMC13171500

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.