Enablers and Barriers of Suicide Risk Assessments and their effects on Clinical Practice Change in Inpatient Healthcare Settings: A Scoping Review
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Résumé
Research Questions: 1) What are the enablers and barriers to implementing inpatient suicide risk assessment screening tools? 2) What effect do suicide risk assessment screening tools have on clinical practice in inpatient settings? Background: Globally, mental health disorders are one of the leading causes of disability and contribute to 90% of suicides in both developed and non-developed countries (Collins & Saxena, 2016; Hidaka, 2012; Ohrnberger et al. 2017). According to the World Health Organization (WHO), 1 in 4 people will either be affected by a mental health disorder or endure a form of mental illness at some point in their lives (Lake, 2017; WHO, 2020). Factors that may affect mental health include: socio-economic pressures, sexual violence, physical illness, emotional stress, and psychiatric disorders (WHO, 2018). As mental health complications have become more prominent in society, there is an increasing demand for effective mental health services (Butler & Pang, 2014; Collins & Saxena, 2016; Patterson & Edwards, 2018), including psychotropic medication, psychotherapy (individual, group, and family), or a combination of both. Individuals with mental health illnesses are at an increased risk of engaging in both self-harm and suicide (Curtis et al. 2018; Wilkinson, 2013). Self-harm is defined as an act of deliberate self-injury regardless of having suicidal motives or intent (Curtis et al. 2018; Hawton et al. 2012; Wilkinson, 2013). In the existing literature, self-injurious activity is often identified as a strong predictor of past, present, and future suicidal ideation (Curtis et al. 2018; Hawton et al. 2012; Wilkinson, 2013). Suicide is defined as a category of death which is unnatural as it is a result of the victim’s own action with the intent to kill themselves (Hawton et al. 2012; Nock et al. 2012). It is estimated that both self-harm and suicide are expressed as coping mechanisms for individuals suffering from intense psychological and/or emotional discomfort (Curtis et al. 2018; Quarshie et al. 2020; Richardson et al. 2007). Global rates of both self-harm incidents and suicide attempts have increased over the past two decades leading these topics to become a major public health concern as approximately one million lives are lost each year (Nock et al. 2012; WHO, 2018;). As the risk of suicide or self-harm within society has become more prevalent, a key component of suicide prevention policies amongst inpatient healthcare settings is suicide risk assessment. Suicide risk assessments have been widely used to evaluate the risk of self-harm and suicide in patients before, during, and after they receive care. These assessments aim to provide clinicians a clearer indication of patients’ risk for suicide and aid in determining the most appropriate course of treatment for these patients (Erbacher & Singer 2018; Sakinofsky 2014). However, there is still a great deal of uncertainty regarding the effectiveness of these risk assessments tools for reducing inpatient self-harm and suicide attempts. Throughout the existing literature, a considerable amount of focus has been directed towards the validation of suicide risk assessment tools. Multiple studies have concentrated on the reliability of current tools for identifying risk instead of assessing the impact of these instruments on actually reducing the levels of suicidality within inpatient settings (Carter et al. 2018; Whiting & Fazel, 2019). Among the existing reviews, conclusions regarding the capabilities of these tools in healthcare settings are conflicting or questionable (Carter et al. 2018; Large et al. 2011). The knowledge surrounding clinical practice patterns following the implementation of suicide risk assessments is sparse. There seems to be an absence of reviews in the existing literature regarding the ability of these assessment tools to impact clinical practice. Aside from categorizing patients as "low" or "high" risk following an assessment, there is a paucity of literature on clinical intervention change while trying to effectively reduce the risk of suicide, within inpatient settings. Therefore, a review identifying the enablers and barriers of suicide risk assessment implementation is necessary. This knowledge can provide an understanding of how these assessments impact changes within clinical practice, thereby being informative for both clinical teams and decision-makers. By determining both the benefits and consequences of these tools on clinical practice, decision-makers can ensure the effective implementation and utilization of these suicide risk screening tools. References: Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32. Butler, M. A., & Pang, M. (2014). Current issues in mental health in Canada: Child and youth mental health. Library of Parliament. Collins, P. Y., & Saxena, S. (2016). Action on mental health needs global cooperation. Nature, 532(7597), 25-27. Curtis, S., Thorn, P., McRoberts, A., Hetrick, S., Rice, S., & Robinson, J. (2018). Caring for young people who self-harm: A review of perspectives from families and young people. International journal of environmental research and public health, 15(5), 950. Erbacher, T. A., & Singer, J. B. (2018). Suicide risk monitoring: The missing piece in suicide risk assessment. Contemporary School Psychology, 22(2), 186-194. Eriksen, M. B., & Frandsen, T. F. (2018). The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. Journal of the Medical Library Association : JMLA, 106(4), 420–431. Hawton, K., Saunders, K. E., & O'Connor, R. C. (2012). Self-harm and suicide in adolescents. The Lancet, 379(9834), 2373-2382. Hidaka, B. H. (2012). Depression as a disease of modernity: explanations for increasing prevalence. Journal of affective disorders, 140(3), 205-214. Lake, J., & Turner, M. S. (2017). Urgent need for improved mental health care and a more collaborative model of care. The Permanente Journal, 21. Nock, M. K., Deming, C. A., Chiu, W. T., Hwang, I., Angermeyer, M., Borges, G., ... & Sampson, N. A. (2012). Mental disorders, comorbidity, and suicidal behavior. Ohrnberger, J., Fichera, E., & Sutton, M. (2017). The relationship between physical and mental health: A mediation analysis. Social Science & Medicine, 195, 42-49. Patterson, J. E., & Edwards, T. M. (2018). An introduction to global mental health. Families, Systems, & Health, 36(2), 137. Quarshie, E. N. B., Waterman, M. G., & House, A. O. (2020). Prevalence of self-harm among lesbian, gay, bisexual, and transgender adolescents: a comparison of personal and social adversity with a heterosexual sample in Ghana. BMC Research Notes, 13(1), 1-6. Sakinofsky, I. (2014). Preventing suicide among inpatients. The Canadian journal of psychiatry, 59(3), 131-140. Wilkinson, P. (2013). Non-suicidal self-injury. European child & adolescent psychiatry, 22(1), 75-79. World Health Organization. (2018). National suicide prevention strategies. Progress, examples and indicators. Retrieved from: https://apps.who.int/iris/bitstream/handle/10665/279765/9789241515016-eng.pdf?ua=1
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,027 |
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 découle