Exploring nurse suicide by firearms: A mixed‐method longitudinal (2003–2017) analysis of death investigations

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Exploring nurse suicide by firearms: A mixed‐method longitudinal (2003–2017) analysis of death investigations

Category: Suicide|Journal: Nursing Forum|Author: C Moutier, F Deskins, G Ye, H Rizzo, J Davidson, S Zisook|Year: 2021

Background

Previously it was noted that firearm use by nurses in suicide was changing. The Center for Disease Control and Prevention suicide dataset contains investigation narratives that no researcher has analyzed and may provide context to inform suicide prevention.

 

Objective

Explore firearm deaths in nurse suicide. Second, test topic modeling techniques to analyze investigation narratives.

 

Methods/Statistical Analysis

Mixed-method retrospective analysis of 739 nurse versus 94,838 nonnurse suicides. Odds ratios (OR) were calculated to determine relative incidence. After tokenization and stop word removal, Latent Dirichlet Analysis and Latent Semantic Indexing topic modeling techniques were applied. Topics were evaluated for clinical significance and content analysis performed.

 

Results

Aim 1: Female nurses used firearms significantly less often than other females between 2003 and 2013 (OR: 0.71; p < .001; 95% confidence interval [CI]: 4.23%–9.83%). A rise in nurse firearm use occurred between 2014 and 2017; with rates now similar to nonnurse females (OR: 0.98; p = .7574; 95% CI: −2.68 to 3.49). Clinically relevant topics identified by topic modeling: preventable deaths, chronic pain, and job loss before suicide.

 

Conclusions

From this research we know that work-related issues in nurse suicides by firearms center around chronic pain, substance use, and job loss. The codes tied to suicidal ideation, previous attempt and/or depression, represented preventable deaths because it is known that if a weapon is removed from the home in these situations a suicide can be aborted. The change in firearm use warrants nurse education regarding firearm safety and suicide prevention. Topic modeling holds promise in focusing analyses of suicide investigations.

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