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Record W6906497556 · doi:10.17605/osf.io/kvwq7

Enablers and Barriers of Suicide Risk Assessments and their effects on Clinical Practice Change in Inpatient Healthcare Settings: A Scoping Review

2021· other· en· W6906497556 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthSuicidal ideationSuicide preventionAffect (linguistics)Occupational safety and healthHealth careHuman factors and ergonomicsClinical Practice

Abstract

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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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.027

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 head0.050
GPT teacher head0.403
Teacher spread0.354 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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