Evidence map›Paper›PMID 42898385›Full record

ArticleFrontiers in psychiatry2026

Understanding non-psychiatric nurses' experiences of caring for patients with mental-physical comorbidity: a descriptive phenomenological study.

Jingxuan Zhang, Yaping Feng, Hong Luo, Bowen Xue

Abstract read
In one paragraph

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

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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

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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

4 authors.

Jingxuan ZhangAffiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yaping FengAffiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Hong LuoAffiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Bowen XueAffiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to explore non-psychiatric nurses' experiences of caring for patients with mental-physical comorbidity, the challenges they encounter, and the factors shaping these experiences. Design: A descriptive phenomenological design was used, with data collected through face-to-face, semi-structured interviews. Methods: Between March and April 2025, 20 registered nurses from non-psychiatric departments of a tertiary hospital in Hangzhou, China, participated in in-depth interviews. Data were analysed using Colaizzi's seven-step phenomenological method, with NVivo 12.0 used to support data management. Results: Nurses described difficulty recognising psychiatric symptoms, balancing mental and physical care, managing safety concerns, communicating with patients, and providing care during night shifts. They also reported limited intervention options and reliance on experiential knowledge. Factors shaping these experiences included stigma-related concealment of mental illness, difficulty engaging some patients in care, limited mental health knowledge and communication skills, lack of standardised guidance, weak collaboration with psychiatric services, and workload and time pressure. Conclusion: Non-psychiatric nurses described caring for patients with mental-physical comorbidity as challenging when psychiatric symptoms were difficult to recognise, mental and physical priorities competed, and access to guidance or psychiatric support was limited. Training in mental health assessment and communication, practical care protocols, and accessible psychiatric consultation may help address these difficulties.

Indexed as

descriptive phenomenologymental health nursingmental-physical comorbiditynon-psychiatric nursesnursing care

Identifiers

PMID42898385
PMCPMC13677930

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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.