Search results

This study aims to build on existing literature on face-to-face aggression in intimate relationships and adopts Finkel’s I³ theory to investigate the relationship between adult attachment style, dispositional self-control and cyber intimate partner aggression (IPA) perpetration and victimization.

Participants (N = 173) 20–52 years of age (M = 32.75 years, SD = 7.73, mode = 29 years) completed a series of standardized online measures to assess anxious and avoidant attachment, dispositional self-control and experience of cyber IPA (psychological, sexual and stalking), as both a perpetrator and victim.

Avoidant attachment was associated with increased perpetration of stalking and psychological abuse. Those high on avoidant attachment were also more likely to report that they were victims of cyber IPA psychological abuse and stalking. Self-control did not predict experience of cyber IPA, as a perpetrator or victim. Interactions between self-control and attachment were also non-significant.

This study addressed the paucity of cyber IPA research conducted with adult populations, by examining processes and factors to improve understanding of the experiences of online perpetration and victimization. The study also found evidence for the importance of impellance factors but not inhibiting factors (Finkel, 2008).

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.