Overview

"A history of child sexual abuse (CSA) is a major risk factor for developing a range of adverse physical, social and mental health problems, including suicidality. Survivors presenting with suicidal phenomena are a high-risk group, making up a significant portion of individuals accessing multiple service systems. Strengthening community support services has been identified as key to enhancing early intervention opportunities for this population. There are no known studies specifically investigating the quality of service responses to young adult women with CSA histories when suicidal. The present research addressed this clinically challenging area by exploring experienced community-based health professionals' perceptions of these young women's service needs. Professionals perceptions of good practice and the systemic factors that constrain or facilitate effective support were investigated. An exploratory, qualitative, interpretive approach involved in- depth interviews with 31 experienced practitioners Data source triangulation was employed to source information from three groups of professionals. A thematic analysis was undertaken, guided by a hermeneutic framework and informed by a gendered lens. It was found that effective pathways of care for these young women were absent. Increasing gender- and trauma-sensitive options of long-term therapeutic support and safe housing in the community sector and expanding at-risk specific ""subacute"" support options were identified as core initiatives to avert the use of costly public acute-care services. Principles of good practice in managing suicidal behaviour indicated that while risk must be monitored, the focus should remain on attending to the factors precipitating distress. This study contributes to informing contemporary policy and practice aimed at creating a more responsive service system for these young women. Identification of core principles of support for effectively attending to suicidal risk and long-term recovery is likely to inform good practice models that can be more rigorously evaluated in future research."

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9781835710364

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