Ethnicity and Health: An Analysis of Physical Health Differences across Twenty-one Ethnocultural Groups in Canada

The study of health differences across a wide-range of ethnic, racial, and cultural groups has received relatively little attention in the literature. Twenty-one ethnocultural groups are examined in the current study, providing one of the most comprehensive analyses to-date on ethnicity and physical health in Canada. Two specific research questions are addressed. First, what is the extent of ethnocultural-based health inequalities in Canada? Second, do ethnocultural differences in health reflect differences in social structural and health-related behavioural environments? These questions are analyzed using the master datafile of the 2000/2001 Canadian Community Health Survey (n=129,588). Three global measures of physical health are used: self-rated health, functional health, and activity restriction. The results show that certain ethnic and cultural groups experience higher health status compared to other ethnocultural groups. Social structural (i.e., socio-demographic and SES factors) and behavioural (alcohol and cigarette consumption, diet/nutrition, and exercise) control variables are also introduced to determine if these factors mediate the relationship between ethnicity/race and health. These findings show that health differences between ethnic and racial groups are partly attributable to structural and behavioural factors. They also show that the mediating effects of these variables vary across ethnocultural groups, and that social structural factors are generally more important than behavioural ones in explaining ethnocultural-based differences in health. The implications of the study findings for future research on ethnicity and health and for health care policies are discussed.ethnicity, race, self-rated health, functional health, social structure, lifestyle

Ethnic Differences in Health: Does Immigration Status Matter?

This study examines health differences between first-generation immigrant and Canadian-born persons who share the same the ethnocultural origin, and the extent to which such differences reflect social structural and health-related behavioural contexts. Data from the 2000/2001 Canadian Community Health Survey show that first generation immigrants of Black and French race/ethnicity tend to have better health than their Canadian-born counterparts, while the opposite is true for those of South Asian, Chinese, and south and east European and Jewish origins. West Asians and Arabs and other Asian groups are advantaged in health regardless of country of birth. Health differences between ethnic foreign- and Canadian-born persons generally converge after adjusting for socio-demographic, SES, and lifestyle factors. Implications for health care policy and program development are discussed.self-rated health; functional health; ethnicity; race; immigration

Comparing Racial and Immigrant Health Status and Health Care Access in Later Life in Canada and the United States

Little comparative research exists on health experiences and conditions of minority groups in Canada and the United States, despite both countries having a racially diverse population with a signifi cant proportion of immigrants. This article explores race and immigrant disparities in health and health care access across the two countries. The study focus was on middle and old age given the change and increasing diversity in health and health care policy, such as Medicare. Logistic regression analysis of data from the 2002–2003 Joint Canada/United States Survey of Health shows that the joint effect of race and nativity on health outcomes – health differences between native and foreign-born Whites and non- Whites – is largely insignifi cant in Canada but considerable in the U.S. Non-White native and foreign-born Americans within both 45-to-64 and 65-and-over age groups experience signifi cant disadvantage in health status and access to care, irrespective of health insurance coverage, demographic, socio-economic, and lifestyle factors.health, obesity, health care, race, immigrant, Canada, United States

Contrasting Inequalities: Comparing Correlates of Health in Canada and the United States

Comparative health studies consistently find that Canadians on average are healthier than Americans. Comparing health status within and between Canada and the United States provides key insights into the distribution of inequalities in these two countries. Canada’s universal health care insurance system contrasts with the mixed system of the United States: universal care for seniors, private health care insurance for many, and no or intermittent coverage for others. These countries are also notably different in the extent of income and racial/ethnic inequalities. It is within this context that this study compares the relative strength of the relationships between social, economic, and demographic factors (sex, age, marital status, income, education, country of birth, and race/ethnicity) and health status in Canada and the United States. Evidence drawn from the 2002-2003 Joint Canada/United States Survey of Health reveals that the correlations between these factors, above all country of birth and race/ethnicity, and health are relatively stronger in the United States, reflecting differences in health care access and racial/ethnic-based inequalities between the countries. The study findings are suggestive of the effects of universal access to health care and more equitable distribution of other social resources in protecting the health of the general population.self-reported health, United States, Canada, health insurance, income, race, ethnicity, age, sex

Advancements and controversies in China’s recent sentencing reforms

This article discusses in detail the content and context of China’s recent sentencing reform and its social, political, and criminal justice implications, as well as its limitations. The focus of China’s criminal justice reforms over the past 37 years has been predominantly on the trial process; the sentencing process has been largely neglected. Revelations of widespread sentencing inconsistency led the Supreme People’s Court (SPC) to initiate sentencing reform in 2005. The intent of the reform was to promote transparency in the sentencing process, ensure consistency in sentencing dispositions, and guard against inappropriate judicial leniency and severity via new sentencing procedural rules and guidelines limiting judges’ sentencing discretion. In addition to discussing the new sentencing procedures and guidelines, this article also examines some hotly debated issues, including whether China’s sentencing process should be completely separate from the trial process; the meaning of ‘sentencing consistency’ in the context of China’s social and political development; and China’s unique sentencing principles in comparison with the practice of some English-speaking jurisdictions

Visual Chirality

How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual data are changed by reflection. We refer to these changes as "visual chirality", after the concept of geometric chirality - the notion of objects that are distinct from their mirror image. Our analysis of visual chirality reveals surprising results, including low-level chiral signals pervading imagery stemming from image processing in cameras, to the ability to discover visual chirality in images of people and faces. Our work has implications for data augmentation, self-supervised learning, and image forensics.Comment: Published at CVPR 2020, Best Paper Nomination, Oral Presentation. Project Page: https://linzhiqiu.github.io/papers/chirality

Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better ${\bf visual}$ dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification.Comment: CVPR 2023. Project website: https://linzhiqiu.github.io/papers/cross_modal

VisualGPTScore: Visio-Linguistic Reasoning with Multimodal Generative Pre-Training Scores

Vision-language models (VLMs) discriminatively pre-trained with contrastive image-text matching losses such as $P(\text{match}|\text{text}, \text{image})$ have been criticized for lacking compositional understanding. This means they might output similar scores even if the original caption is rearranged into a different semantic statement. To address this, we propose to use the ${\bf V}$isual ${\bf G}$enerative ${\bf P}$re-${\bf T}$raining Score (${\bf VisualGPTScore}$) of $P(\text{text}|\text{image})$, a $\textit{multimodal generative}$ score that captures the likelihood of a text caption conditioned on an image using an image-conditioned language model. Contrary to the belief that VLMs are mere bag-of-words models, our off-the-shelf VisualGPTScore demonstrates top-tier performance on recently proposed image-text retrieval benchmarks like ARO and Crepe that assess compositional reasoning. Furthermore, we factorize VisualGPTScore into a product of the $\textit{marginal}$ P(text) and the $\textit{Pointwise Mutual Information}$ (PMI). This helps to (a) diagnose datasets with strong language bias, and (b) debias results on other benchmarks like Winoground using an information-theoretic framework. VisualGPTScore provides valuable insights and serves as a strong baseline for future evaluation of visio-linguistic compositionality.Comment: Website: https://linzhiqiu.github.io/papers/visual_gpt_score/ Code: https://github.com/linzhiqiu/visual_gpt_score