Enhancing Person‐Centred Care in Suicide Prevention: A Nursing Perspective
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,013 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».