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Record W4408547956 · doi:10.1111/jpm.13168

Enhancing Person‐Centred Care in Suicide Prevention: A Nursing Perspective

2025· review· en· W4408547956 on OpenAlexaff
Matias Gay

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2025
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsIntervention (counseling)Perspective (graphical)NarrativePsychologyMedicineNursingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Suicide prevention within nursing has historically been dominated by biomedical models that emphasize risk assessment and symptom management. While these frameworks offer structure and liability reduction, they often fail to capture the deeply personal and existential dimensions of suicidality. The reliance on predictive tools with modest accuracy, such as the Columbia-Suicide Severity Rating Scale (C-SSRS), has led to a gap between assessment and meaningful intervention. Critics argue that this model fosters a procedural approach that discourages patient disclosure and limits therapeutic engagement. In contrast, person-centered care (PCC) emphasizes relational trust, individualized understanding, and the integration of patient narratives into clinical decision-making. This paper examines the need to shift from standardized, symptom-focused approaches toward a dynamic, patient-centered framework. METHODS: This paper critically evaluates the limitations of biomedical suicide prevention strategies by synthesizing theoretical contributions from key suicidologists, including Edwin Shneidman, Antoon Leenaars, Konrad Michel, Igor Galynker, and David Jobes. Evidence-based, person-centered models such as the Collaborative Assessment and Management of Suicide (CAMS) and the Narrative Crisis Model (NCM) are explored in contrast to traditional suicide risk assessments. Additionally, barriers to implementing PCC in nursing-such as time constraints, administrative demands, and gaps in professional training-are examined. RESULTS: While biomedical models provide standardized risk management strategies, their over-reliance on quantifiable indicators fails to address suicidality's multidimensional nature. The predictive limitations of suicide screening tools often lead to overestimation or underestimation of risk, increasing the likelihood of missed intervention opportunities. Furthermore, systemic factors such as high-acuity environments and compassion fatigue contribute to nurses' challenges in engaging with person-centered interventions. Models like CAMS and NCM have demonstrated greater efficacy in fostering trust, enhancing clinical engagement, and addressing the subjective experiences of suicidal individuals, ultimately improving outcomes. CONCLUSIONS: The limitations of traditional biomedical approaches underscore the necessity of integrating person-centered care into nursing practice. Suicide prevention should not be dictated solely by standardized risk assessments but should instead prioritize therapeutic alliance, empathy, and the co-construction of meaning. Nurses, given their frontline role in patient care, are uniquely positioned to transform suicide prevention through narrative-based interventions and compassionate engagement. However, achieving this paradigm shift requires institutional support, expanded nursing education, and systemic recognition of the importance of relational care. This paper advocates for a holistic approach that moves beyond risk prediction toward meaningful, person-centered interventions that address the lived experiences and psychological distress of individuals at risk for suicide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.013
Scholarly communication0.0070.004
Open science0.0020.009
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.439
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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