Gender identity is a core component of human experience, critical to account for in broad health, development, psychosocial research, and clinical practice. Yet, the psychometric characterization of gender has been impeded due to challenges in modeling the myriad gender self-descriptors, statistical power limitations related to multigroup analyses, and equity-related concerns regarding the accessibility of complex gender terminology. Therefore, this initiative employed an iterative multi-community-driven process to develop the Gender Self-Report (GSR), a multidimensional gender characterization tool, accessible to youth and adults, nonautistic and autistic people, and gender-diverse and cisgender individuals. In Study 1, the GSR was administered to 1,654 individuals, sampled through seven diversified recruitments to be representative across age (10–77 years), gender and sexuality diversity (∼33% each gender diverse, cisgender sexual minority, cisgender heterosexual), and autism status (>33% autistic). A random half-split subsample was subjected to exploratory factor analytics, followed by confirmatory analytics in the full sample. Two stable factors emerged: Nonbinary Gender Diversity and Female–Male Continuum (FMC). FMC was transformed to Binary Gender Diversity based on designated sex at birth to reduce collinearity with designated sex at birth. Differential item functioning by age and autism status was employed to reduce item–response bias. Factors were internally reliable. Study 2 demonstrated the construct, convergent, and ecological validity of GSR factors. Of the 30 hypothesized validation comparisons, 26 were confirmed. The GSR provides a community-developed gender advocacy tool with 30 self-report items that avoid complex gender-related “insider” language and characterize diverse populations across continuous multidimensional binary and nonbinary gender traits.
Issues: Opioid overdose kills over 100,000 people each year globally. Mobile health (mHealth) technologies and devices with the capacity to prevent, detect or respond to opioid overdose already exist or could be designed. These technologies may particularly help those who use alone. For technologies to be successful, they must be effective and acceptable to the at-risk population. The aim of this scoping review is to identify published studies on mHealth and wearable technologies that attempt to prevent, detect or respond to opioid overdose.Approach: A systematic scoping review and hand-searching of literature was conducted up to October 2022. APA PsychInfo, Embase, Web of Science and Medline databases were searched. To be included, articles had to report on (i) mHealth technologies that deal with (ii) opioid (iii) overdose.Key Findings: 348 records were identified, with 14 studies eligible for this review across four domains: 1) acceptability/willingness to use overdose-related technologies/devices (five); 2) devices that use biometric data to detect overdose (five); 3) technologies that require intervention/response from others (four); 4) devices that automatically respond to an overdose with administration of an antidote (three).Implications: There are multiple routes in which these technologies may be deployed, but several factors impact acceptability (e.g. discretion or size) and accuracy of detection (e.g. sensitive parameter/threshold with low false positive rate). Conclusion: mHealth technologies for opioid overdose may play a crucial role in responding to the ongoing global opioid crises. This scoping review identifies vital research that will determine the future success of these technologies.
Autism spectrum disorder (ASD) without intellectual disability is diagnosed later and with greater difficulty in females relative to males. For autistic girls and women, the journey to an autism diagnosis may include one or more misdiagnoses. Misdiagnosis with borderline personality disorder (BPD) or borderline traits may be particularly common, and characteristics often observed in autistic girls and women may contribute specifically to a risk of misdiagnosis with BPD. This review draws from a burgeoning literature on ASD in girls and women to provide a detailed discussion of differential diagnosis of BPD and ASD in cisgender females, with a focus on phenotypic features and/or their presentation that are common in autistic females and that may be particularly prone to miscategorization as BPD. Distinctions between ASD and BPD are identified, emphasizing the need for scrutiny of an individual’s clinical presentation to tease apart potentially subtle yet superficial differences between the ASD and BPD phenotypes, which belie more obvious, deeper differences in diagnostic meanings. We highlight instances in which similar symptom expression may be driven by differing underlying factors. Implications for the distinction of ASD and BPD/borderline traits in informing appropriate therapeutic intervention are discussed.
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