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\"Through empirical material as well as theoretical discussions, this book explores developments in gender-technology relations from the 1980s to today. The author draws on her long-lasting research in the field, providing insight in both historical and more recent discussions of gender in relation to computers and computing\"-- Provided by publisher.

Health care wearable devices can transform health care delivery by enabling real-time, continuous monitoring that facilitates early disease detection, personalized treatments, and improved patient engagement. The COVID-19 pandemic has heightened awareness of the importance of health technology, accelerating interest in wearables as tools for monitoring health and managing chronic conditions. As we navigate the postpandemic era, understanding the adoption and data-sharing behaviors associated with wearable devices has become increasingly critical. Despite their potential, challenges and low adoption rates persist, with significant gaps in understanding the impact of sociodemographic factors, health conditions, and digital literacy on the use and data-sharing behaviors of these devices. This study aimed to explore the usage and data-sharing practices (willingness to share wearable data and actual data-sharing behavior) of wearable devices among US adults specifically during the later phases of the COVID-19 pandemic. Using cross-sectional data from the National Cancer Institute's Health Information National Trends Survey 6, conducted from March to November 2022, this study uses responses from 5591 US adults to examine wearable use, willingness to share wearable data with providers, family, and friends, and the wearable data-sharing behavior. The results indicate an increase in wearable device adoption to 36.36% (2033/5591) in 2022, up from 28%-30% in 2019. We also find a significant discrepancy between the willingness to share data, with 78.4% (1584/2020) of users open to sharing with health care providers, and the actual sharing behavior, where only 26.5% (535/ 2020) have done so. Higher odds of using wearables were associated with female gender (odds ratio [OR] 1.49, 95% CI 1.17-1.90, P<.01) and higher income levels (OR 2.65, 95% CI 1.42-4.93, P<.01 for incomes between US $50,000 and US $75,000, and OR 3.2, 95% CI 1.71-5.97, P<.01 for incomes above US $75,000). However, the likelihood of usage and data sharing declines significantly with age. Compared with African American respondents, Hispanic respondents were more willing to share wearable data with providers (OR 1.92, 95% CI 1.02-3.62, P<.05), though the odds of their actual sharing of wearable data with providers was relatively less (OR 0.44, 95% CI 0.20-0.97, P<.05). Frequency of provider visits (OR 1.23, 95% CI 1.08-1.39, P<.01), and total medical conditions (OR 1.35, 95% CI 1.05-1.73, P<.01) were significant predictors of data-sharing behavior. The study also identified weight, frequency of provider visits, technological self-efficacy and frequent physical activity as predictors for higher wearable use. Insights from this study are crucial for health care providers and policy makers aiming to leverage wearable technology to enhance health outcomes. Addressing the disparities and barriers identified can lead to more effective integration of these technologies in health care systems, thereby maximizing the potential of digital health tools to improve public health outcomes.

Are gender differences in face recognition influenced by familiarity and socio-cultural factors? Previous studies have reported gender differences in processing unfamiliar faces, consistently finding a female advantage and a female own-gender bias. However, researchers have recently highlighted that unfamiliar faces are processed less efficiently than familiar faces, which have more robust, invariant representations. To-date, no study has examined whether gender differences exist for familiar face recognition. The current study addressed this by using a famous faces task in a large, web-based sample of  > 2000 participants across different countries. We also sought to examine if differences varied by socio-cultural gender equality within countries. When examining raw accuracy as well when controlling for fame, the results demonstrated that there were no participant gender differences in overall famous face accuracy, in contrast to studies of unfamiliar faces. There was also a consistent own-gender bias in male but not female participants. In countries with low gender equality, including the USA, females showed significantly better recognition of famous female faces compared to male participants, whereas this difference was abolished in high gender equality countries. Together, this suggests that gender differences in recognizing unfamiliar faces can be attenuated when there is enough face learning and that sociocultural gender equality can drive gender differences in familiar face recognition.

With the increasing integration of artificial intelligence (AI) in healthcare, concerns about bias in AI models have emerged, particularly regarding demographic factors. In medical imaging, biases in training datasets can significantly impact diagnostic accuracy, leading to unequal healthcare outcomes. This study assessed the impact of sex bias on AI models for diagnosing knee joint recess distension using ultrasound imaging. We utilized a retrospective dataset from community clinics across Canada, comprising 5,000 de-identified MSKUS images categorized by sex and clinical findings. Two binary convolutional neural network (BCNN) classifiers were developed to detect synovial recess distension and determine patient sex. The dataset was balanced across sex and joint recess distension, with models trained using advanced data augmentation and validated through both individual and mixed demographic scenarios using a 5-fold cross-validation strategy. Our BCNN classifiers showed that AI performance varied significantly based on the training data’s demographic characteristics. Models trained exclusively on female datasets achieved higher sensitivity and accuracy but exhibited decreased specificity when applied to male images, suggesting a tendency to overfit female-specific features. Conversely, classifiers trained on balanced datasets displayed enhanced generalizability. This was evident from the classification heatmaps, which varied less between sexes, aligning more closely with clinically relevant features. The study highlights the critical influence of demographic biases on the diagnostic accuracy of AI models in medical imaging. Our results demonstrate the necessity for thorough cross-demographic validation and training on diverse datasets to mitigate biases. These findings are based on a supervised CNN model; evaluating whether they extend to other architectures, such as self-supervised learning (SSL) methods, foundation models, and Vision Transformers (ViTs), remains a direction for future research.

Background Large language models (LLMs) are being used to reduce the administrative burden in long-term care by automatically generating and summarising case notes. However, LLMs can reproduce bias in their training data. This study evaluates gender bias in summaries of long-term care records generated with two state-of-the-art, open-source LLMs released in 2024: Meta’s Llama 3 and Google Gemma. Methods Gender-swapped versions were created of long-term care records for 617 older people from a London local authority. Summaries of male and female versions were generated with Llama 3 and Gemma, as well as benchmark models from Meta and Google released in 2019: T5 and BART. Counterfactual bias was quantified through sentiment analysis alongside an evaluation of word frequency and thematic patterns. Results The benchmark models exhibited some variation in output on the basis of gender. Llama 3 showed no gender-based differences across any metrics. Gemma displayed the most significant gender-based differences. Male summaries focus more on physical and mental health issues. Language used for men was more direct, with women’s needs downplayed more often than men’s. Conclusion Care services are allocated on the basis of need. If women’s health issues are underemphasised, this may lead to gender-based disparities in service receipt. LLMs may offer substantial benefits in easing administrative burden. However, the findings highlight the variation in state-of-the-art LLMs, and the need for evaluation of bias. The methods in this paper provide a practical framework for quantitative evaluation of gender bias in LLMs. The code is available on GitHub.

Sex differences in cancer are well-established. However, less is known about sex differences in diagnosis of brain metastasis and outcomes among patients with advanced melanoma. Using a United States nationwide electronic health record-derived de-identified database, we evaluated patients diagnosed with advanced melanoma from 1 January 2011–30 July 2022 who received an oncologist-defined rule-based first line of therapy (n = 7969, 33% female according to EHR, 35% w/documentation of brain metastases). The odds of documented brain metastasis diagnosis were calculated using multivariable logistic regression adjusted for age, practice type, diagnosis period (pre/post-2017), ECOG performance status, anatomic site of melanoma, group stage, documentation of non-brain metastases prior to first-line of treatment, and BRAF positive status. Real-world overall survival (rwOS) and progression-free survival (rwPFS) starting from first-line initiation were assessed by sex, accounting for brain metastasis diagnosis as a time-varying covariate using the Cox proportional hazards model, with the same adjustments as the logistic model, excluding group stage, while also adjusting for race, socioeconomic status, and insurance status. Adjusted analysis revealed males with advanced melanoma were 22% more likely to receive a brain metastasis diagnosis compared to females (adjusted odds ratio [aOR]: 1.22, 95% confidence interval [CI]: 1.09, 1.36). Males with brain metastases had worse rwOS (aHR: 1.15, 95% CI: 1.04, 1.28) but not worse rwPFS (adjusted hazard ratio [aHR]: 1.04, 95% CI: 0.95, 1.14) following first-line treatment initiation. Among patients with advanced melanoma who were not diagnosed with brain metastases, survival was not different by sex (rwOS aHR: 1.06 [95% CI: 0.97, 1.16], rwPFS aHR: 1.02 [95% CI: 0.94, 1.1]). This study showed that males had greater odds of brain metastasis and, among those with brain metastasis, poorer rwOS compared to females, while there were no sex differences in clinical outcomes for those with advanced melanoma without brain metastasis.

Background Lipids represent essential components of extracellular vesicles (EVs), playing structural and regulatory functions during EV biogenesis, release, targeting, and cell uptake. Importantly, lipidic dysregulation has been linked to several disorders, including metabolic syndrome, inflammation, and neurological dysfunction. Our recent results demonstrated the involvement of plasma EV microRNAs as possible amplifiers and biomarkers of neuroinflammation and brain damage induced by ethanol intoxication during adolescence. Considering the possible role of plasma EV lipids as regulatory molecules and biomarkers, we evaluated how acute ethanol intoxication differentially affected the lipid composition of plasma EVs in male and female adolescents and explored the participation of the immune response. Methods Plasma EVs were extracted from humans and wild-type (WT) and Toll-like receptor 4 deficient (TLR4-KO) mice. Preprocessing and exploratory analyses were conducted after the extraction of EV lipids and data acquisition by mass spectrometry. Comparisons between ethanol-intoxicated and control human female and male individuals and ethanol-treated and untreated WT and TLR4-KO female and male mice were used to analyze the differential abundance of lipids. Annotation of lipids into their corresponding classes and a lipid set enrichment analysis were carried out to evaluate biological functions. Results We demonstrated, for the first time, that acute ethanol intoxication induced a higher enrichment of distinct plasma EV lipid species in human female adolescents than in males. We observed a higher content of the PA, LPC, unsaturated FA, and FAHFA lipid classes in females, whereas males showed enrichment in PI. These lipid classes participate in the formation, release, and uptake of EVs and the activation of the immune response. Moreover, we observed changes in EV lipid composition between ethanol-treated WT and TLR4-KO mice (e.g., enrichment of glycerophosphoinositols in ethanol-treated WT males), and the sex-based differences in lipid abundance are more notable in WT mice than in TLR4-KO mice. All data and results generated have been made openly available on a web-based platform ( http://bioinfo.cipf.es/sal ). Conclusions Our results suggest that binge ethanol drinking in human female adolescents leads to a higher content of plasma EV lipid species associated with EV biogenesis and the propagation of neuroinflammatory responses than in males. In addition, we discovered greater differences in lipid abundance between sexes in WT mice compared to TLR4-KO mice. Our findings also support the potential use of EV-enriched lipids as biomarkers of ethanol-induced neuroinflammation during adolescence. Highlights Ethanol induces a differential enrichment of plasma EV lipid species in human and murine female adolescents compared to males. The function of these lipid species suggests that binge alcohol drinking in human female adolescents could prompt elevated EV biogenesis and a more significant immune response than in males. WT mice display more significant disparities in lipid abundance between sexes than TLR4-KO mice. The study takes a novel approach—based on bioinformatic analysis of lipidomic data—to studying the sex-based differences in the effect of alcohol. Plasma EV lipids represent suitable non-invasive biomarker candidates and could help to explain the mechanisms underlying the neuroinflammatory response after acute intoxication. Plain language summary Lipids represent essential components of extracellular vesicles (EVs), playing structural and regulatory functions during EV biogenesis, release, targeting, and cell uptake. Lipidic dysregulation has been linked to several disorders. We evaluated how acute ethanol intoxication differentially affected the lipid composition of plasma EVs in male and female adolescents and explored the participation of the immune response. Plasma EVs were extracted from humans and wild-type (WT) and Toll-like receptor 4 deficient (TLR4-KO) mice. Preprocessing and exploratory analyses were conducted after the extraction of EV lipids and data acquisition by mass spectrometry. Our analysis of differential abundance demonstrated, for the first time, that acute ethanol intoxication induced a higher enrichment of distinct plasma EV lipid species in human female adolescents than in males. We observed a higher content of the PA, LPC, unsaturated FA, and FAHFA lipid classes in females, whereas males showed enrichment in PI. These lipid classes participate in the formation, release, and uptake of EVs and the activation of the immune response. Moreover, we observed changes in EV lipid composition between ethanol-treated WT and TLR4-KO mice (e.g., enrichment of glycerophosphoinositols in ethanol-treated WT males), and the sex-based differences in lipid abundance are more notable in WT mice than in TLR4-KO mice. All data and results generated have been made openly available on a web-based platform ( http://bioinfo.cipf.es/sal ). Our findings also support the potential use of EV-enriched lipids as biomarkers of ethanol-induced neuroinflammation during adolescence.

BACKGROUND:Despite national screening efforts, military sexual trauma (MST) is underreported. Little is known of racial/ethnic differences in MST reporting in the Veterans Health Administration (VHA). OBJECTIVE:This study aimed to compare patterns of MST disclosure in VHA by race/ethnicity. RESEARCH DESIGN:Retrospective cohort study of MST disclosures in a national, random sample of Veterans who served in Afghanistan and Iraq and completed MST screens from October 2009 to 2014. We used natural language processing (NLP) to extract MST concepts from electronic medical notes in the year following Veterans’ first MST screen. MEASURE(S):Any evidence of MST (positive MST screen or NLP concepts) and late MST disclosure (NLP concepts following a negative MST screen). Multivariable logistic regressions, stratified by sex, tested racial/ethnic differences in any MST evidence, and late disclosure. RESULTS:Of 6618 male and 6716 female Veterans with MST screen results, 1473 had a positive screen (68 male, 1%; 1405 female, 21%). Of those with a negative screen, 257 evidenced late MST disclosure by NLP (44 male, 39%; 213 female, 13%). Late MST disclosure was usually documented during mental health visits. There were no significant racial/ethnic differences in MST disclosure among men. Among women, blacks were less likely than whites to have any MST evidence (adjusted odds ratio=0.75). In the subsample with any MST evidence, black and Hispanic women were more likely than whites to disclose MST late (adjusted odds ratio=1.89 and 1.59, respectively). CONCLUSIONS:Combining NLP results with MST screen data facilitated the identification of under-reported sexual trauma experiences among men and racial/ethnic minority women.

Background. Exposure to advertisements for tobacco products and tobacco warning labels evokes emotions. This study evaluated the association of discrete positive and negative emotions with interest in alternative tobacco products. Method. In 2013, 1,226 U.S. adult nonsmokers and current smokers viewed advertisements for moist snuff, snus, and electronic cigarettes (e-cigarettes) with various warning labels and then indicated their emotional responses in terms of anger, anxiety, sadness, guilt, disgust, discouragement, hope, and contentment. Outcomes were openness to using moist snuff, snus, and e-cigarettes in the future and interest in a free sample of each product. Data were analyzed in 2016. Results. Hope was positively associated with openness and interest across all alternative tobacco products as was contentment for moist snuff and snus. Anger was negatively associated with openness to moist snuff and e-cigarettes, disgust negatively to moist snuff and snus, and anxiety negatively to e-cigarettes. Being a current smoker, ever trying a corresponding product, being male, and younger age were associated with greater openness to and interest in moist snuff and snus. For e-cigarettes, being a current smoker, ever trying e-cigarettes, and being female were associated with greater openness, and being a current smoker was associated with greater odds of selecting a free sample. Conclusions. Positive emotions, particularly hope, were consistently positively associated with interest in alternative tobacco products. Hope is widely used by tobacco and e-cigarette companies to advertise their products. Antitobacco messages should aim to lower hope associated with tobacco products but increase hope for cessation or life without tobacco.

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.