Although girls and women represent only a minority of the forensic mental health and prison populations, studies worldwide suggest that there has been a steady increase in the number of females being convicted for committing offenses, especially violent offenses. In this chapter, an overview will be provided on the specific risks and needs of female offenders and the relevance of gender-responsive treatment in forensic mental health services. First, the literature into the prevalence and nature of offending by females will be reviewed, with a focus on violent offending. Next, the most recent knowledge in the field will be summarized with respect to gender-sensitive risk assessment and gender-responsive treatment in forensic mental health care. Finally, some recommendations will be provided for mental health professionals working with females in forensic mental health services and for future research.
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Women and girls represent only a minority in the penitentiary system and in forensic mental health care. About 6%–10% of both prison and forensic psychiatric populations in Western countries comprise women (see for the most recent offi cial statistics in the UK w ww.gov. uk/government, in Canada w ww.statcan.gc.ca, and in the US w ww.bjs.gov) . However, there seems to be widespread agreement that in the past 20 years female offending has been on the rise, especially violent offending and particularly among young women ( Miller, Malone, and Dodge, 2010; M oretti, Catchpole, and Odgers, 2005) . Overall, a disproportionate growth of females entering the criminal justice system and forensic mental health care has been observed in many countries (for reviews, see Nicholls, Cruise, Greig, and Hinz, 2015; Odgers, Moretti, and Reppucci, 2005 ; Walmsley, 2015) . In addition, it should be noted that the ‘dark number’ for women is suggested to be bigger than for men. Offi cial prevalence rates of female offending might constitute an underestimation as women usually commit less reported offences, for example, domestic violence (N icholls, Greaves, Greig, and Moretti, 2015) . Furthermore, it has been found that – if apprehended – girls and women are treated more leniently by professionals and the criminal justice system. Generally, they receive lower prison sentences and are more often admitted to civil psychiatric institutions instead of receiving a prison sentence or mandatory forensic treatment after committing violence ( Javdani, Sadeh, and Verona, 2011 ; Jeffries, Fletcher, and Newbold, 2003 ). Hence, although female offenders compared to male offenders are a minority, female violence is a substantial problem that deserves more attention. Our understanding of female offenders is hindered by the general paucity of theoretical and empirical investigations of this population. In order to improve current treatment and assessment practices, our knowledge and understanding of female offenders should be enlarged and optimised (d e Vogel and Nicholls, 2016 ).
The aim of this study is to assess information on voice quality features and to ascertain the variability of these features in specified groups. Groups were created based on gender and status of vocal training, in order to study the influence of these grouping variables on selected voice quality features. Gender was chosen as a grouping variable, because previous investigations clearly demonstrated differences in voice quality characteristics between men and women. These differences have implications for the creation of a normative database, concerning its proposed function as a frame of reference. Vocal training was intentionally introduced to give direction to what might be regarded as good vocal characteristics, as compared to characteristics of subjects without vocal training. Characteristics of the vocal apparatus and voice quality features can be acquired in many ways. Four practicable methods, easily employed in a clinical environment and extensively outlining the vocal apparatus and voice function, are used in this study. Results of these investigations are described in the following chapters.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
