Browsing by Author "Holman, Mikayla"

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    Suicide risk and protective factors : a network approach : a thesis presented in fulfilment of the requirements for the degree of Master of Science (by thesis) in Psychology at Massey University, Albany, New Zealand
    (Massey University, 2019) Holman, Mikayla
    Suicide is a complex phenomenon, with numerous factors contributing to an individual’s risk of suicide. To visualise and quantify complex interactions between variables, a novel approach called network analysis can be used. The aim of the present study was to explore how risk and protective factors for suicide interact with one another, and to determine which factors were most central to a network of these factors. Using an online survey, cross- sectional data was collected from a sample of 515 individuals who lived in New Zealand, Australia, the United Kingdom, and the United States of America, who were recruited through either social media or Prolific Academic. A network of 18 risk and protective factors for suicide was estimated using network analysis. In the network, suicidal ideation was strongly related to the suicide risk factors of feeling depressed, anxious, and hopeless, as well as substance use and perceived burdensomeness. In contrast, self-esteem, resilience, access to mental health services and a positive attitude towards these services were each protective against suicidal ideation. Factors which had the highest strength centrality were feeling depressed, feeling hopeless, perceived burdensomeness, self-esteem, and social support. The results of this research emphasise the importance of examining protective factors as well as risk factors when determining an individual’s suicide risk.
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    A temporal network analysis of risk factors for suicide : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Clinical Psychology at Massey University, Manawatū, New Zealand
    (Massey University, 2024-03-19) Holman, Mikayla
    Suicide is a major public health concern in New Zealand, with the number of lives lost due to suicide increasing almost every year. The factors influencing a person’s decision to take their own life are numerous and often complex. Some of these factors are dynamic, fluctuating over short periods of time and ultimately altering a person’s risk for suicide. Network analysis is a novel statistical technique that can be used to explore complex causal associations in systems of variables, such as risk factors for suicide. The present study used temporal network analysis to explore the associations between dynamic risk factors for suicide over time. Data collection involved ecological momentary assessment, where a general community sample of 39 adult participants completed a brief survey five times per day for ten days, resulting in 1420 completed surveys. Each survey assessed participants’ momentary experience of suicidal ideation, depressed mood, hopelessness, social support, self-esteem, thwarted belongingness, perceived burdensomeness, anhedonia, worthlessness, alcohol intoxication, and fatigue. All variables fluctuated from measurement to measurement at least some of the time, highlighting the dynamic nature of suicide risk. Temporal, contemporaneous, and between-persons networks of the 11 measured variables were estimated using temporal network analysis. In the temporal network, hopelessness was the only variable that predicted an increase in suicidal ideation at the subsequent measurement. Multiple nodes were estimated to be positively associated with suicidal ideation in the contemporaneous network, including depressed mood, thwarted belongingness, perceived burdensomeness, and worthlessness, while self-esteem was negatively associated with suicidal ideation in this network. In the between-persons network, hopelessness was the only variable with a significant association with suicidal ideation. The results of this study highlight the importance of continuously assessing changes in suicide risk factors given their dynamic nature. Hopelessness may be an especially important risk factor to assess given its temporal association with increased suicidal ideation. Regarding future research opportunities, experimental N=1 network studies about the effectiveness of personalised interventions based on node centrality are an important next step in determining whether individualised networks have a place in personalised treatment for suicidality.