Do you feel confident enough to seek help: The relationship between mental health confidence and help-seeking intentions and help-seeking in military members

Abstract: The literature review supports that when looking at help-seeking behaviors, specifically in the military population, there is a significant amount of service members who endorse mental health concerns or distress but do not seek help for their mental health (Hom et al.,2017). The range of mental health concerns includes depression, anxiety, adjustment disorder, posttraumatic stress, suicidal ideations, and substance abuse behaviors, to name a few (Waitzkin et al., 2018). Physiological concerns may also arise when mental health concerns are not attended to within the military, which primarily has been supported to be exhibited as unsolicited weight gain, insomnia, and maladaptive coping strategies (Maclean, 2012; Heyat et al., 2021). A sample of 102 adult participants (ages 18-59) were recruited from a Southern state Army National Guard unit. For the first research question, a linear regression model was utilized to investigate the relationship between general self-efficacy and mental health confidence. The first research question asked: Does general self-efficacy have a relationship to mental health confidence in military members? The second research question utilized a multivariate analysis. The second research question investigated: Does mental health confidence have a relationship with help-seeking intentions and/or help-seeking attitudes? The third research question utilized the analysis of indirect effects. The third research question investigated: Does military stigma and self-reliance indirectly affect the relationship between mental health confidence and help-seeking intentions and/or help-seeking attitudes? Measures included were the General Self-Efficacy Scale, Mental Health Confidence Scale, Help-Seeking Intention Scale, Help-Seeking Attitude Scale, Military Stigma Scale, Self-Reliance Scale, and a series of military service-related questions and demographic items. The gathered data was analyzed utilizing linear regression and indirect effect path analysis. Results from the Analyses chapter support the idea that general self-efficacy has a significant and positive relationship to mental health confidence. Mental health confidence did not significantly predict help-seeking intentions or help-seeking attitudes. Mental health confidence does have a significant and positive effect on help-seeking intentions. Implications and considerations for theory, clinical practice, and future research are discussed.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.