Evidence mapPaperPMID 42272064Full record

ArticleInternational journal of mental health nursing2026

How Does Artificial Intelligence Align With Person-Centred Principles in Mental Health Nursing? A Scoping Review.

Carmel Bond, Adrianna Lorraine Watson, Graeme D Smith, Helen Aveyard, Debra Jackson

Abstract readScoping Review
In one paragraph

Article in International journal of mental health nursing, 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
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
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

5 authors.

Carmel BondDepartment of Nursing and Midwifery, School of Health and Social Care, Sheffield Hallam University, Sheffield, UK.ORCID https://orcid.org/0000-0002-9945-8577
Adrianna Lorraine WatsonCollege of Nursing, Brigham Young University, Provo, Utah, USA.ORCID https://orcid.org/0000-0002-0134-0520
Graeme D SmithS.K. Yee School of Health Sciences, Saint Francis University, Tseung Kwan O, Hong Kong.
Helen AveyardOxford Brookes University, Oxford, UK.
Debra JacksonSchool of Nursing, University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-5252-5325

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is increasingly used in mental health nursing, yet its alignment with person-centred care remains underexplored. This scoping review examines the extent to which artificial intelligence applications in mental health nursing align with person-centred principles, and where tensions and risk emerge. A systematic search of three electronic databases identified studies published since 2018. Data were charted for study characteristics, artificial intelligence modalities, person-centred care concepts addressed, and research gaps. Findings show growing interest in technology-enabled care delivery, monitoring, and decision support in mental health settings, with varying degrees of attention to person-centred values such as empathy, shared decision-making, dignity and therapeutic alliances. However, considerable gaps remain regarding ethical integration, digital therapeutic relationships, trust, surveillance and the measurement of person-centred care outcomes. This review maps the current evidence base and highlights critical gaps in understanding how artificial intelligence reshapes therapeutic relationships, professional roles and power dynamics in mental health nursing. Future research is needed to ensure that the adoption of artificial intelligence is not only safe and effective, but also ethically grounded and aligned with the humanistic foundations of person-centred mental health nursing.

Indexed as

Artificial IntelligencePatient-Centered CarePsychiatric NursingHumansartificial intelligencemental healthnursingperson‐centred carepsychiatric nursing

Identifiers

PMID42272064
PMCPMC13254240

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.