Dataset Information

Multiracial Children's and Adults' Categorizations of Multiracial Individuals.

ABSTRACT: Research has explored how multiracial individuals are categorized by monoracial individuals, but has not yet explored how they are categorized by multiracial individuals themselves. We examined how multiracial children (4-9 years) and adults categorized multiracial targets (presented with and without parentage information). When parentage information was provided, multiracial targets were more likely to be categorized as neither wholly black nor wholly white. However, both multiracial adults and children more often categorized multiracial targets as black than as white regardless of the absence or presence of parentage information. For multiracial children, increased contact with white people predicted the tendency to categorize multiracial targets as black. These data suggest that multiracial children's categorizations are more flexible than those of monoracial children in previous research, and that the tendency to categorize multiracial targets as black emerges early in development within multiracial samples, and is especially likely in predominantly white contexts.

SUBMITTER: Roberts SO

PROVIDER: S-EPMC5588871 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Multiracial Children's and Adults' Categorizations of Multiracial Individuals.

Roberts Steven Othello SO Gelman Susan S

Journal of cognition and development : official journal of the Cognitive Development Society 20160223 1

Research has explored how multiracial individuals are categorized by monoracial individuals, but has not yet explored how they are categorized by multiracial individuals themselves. We examined how multiracial children (4-9 years) and adults categorized multiracial targets (presented with and without parentage information). When parentage information was provided, multiracial targets were more likely to be categorized as neither wholly black nor wholly white. However, both multiracial adults and ...[more]

PMID: 28890668

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Project description:Background/objectivesPrenatal growth, which is widely marked by birthweight, may have a pivotal role in affecting the lifelong risk of cardiometabolic disorders; however, comprehensive evaluation of its relations with childhood cardiometabolic risk patterns and the ethnic and gender disparities in national representative populations is still lacking. The aim of this study was to evaluate the associations between birthweight and comprehensive patterns of cardiometabolic risk in a nationally representative sample of children and adolescents.Subjects/methodsProspective analyses were performed using data from 28 153 children 0 to 15 years in the National Health and Nutrition Examination Survey from 1999 through 2014. We defined childhood cardiometabolic disorders using standard definitions for obesity, high blood pressure, hyperglycemia and dyslipidemia.ResultsFive birthweight categories <2.5, 2.5-3.0, 3.0-3.5, 3.5-4.2 and ⩾4.2 kg accounted for 8.2%, 17.9%, 35.7%, 27.9% and 10.4% of the population, respectively. In all children, with increasing birthweight, we observed significantly increasing trends of the risk of general and central obesity (P for trend <0.01) and significantly decreasing trends of the risk of high systolic blood pressure (SBP), high HbA1c and low high-density lipoprotein cholesterol (HDL-C) (P for trend <0.05). The associations were independent of current body mass index (BMI). In addition, we found that the relations of birthweight with high waist circumference in Black children showed U-shape, as well as high SBP in Mexican and Hispanic children. Moreover, we found that the associations of low birthweight with high SBP and low HDL-C appeared to more prominent significant in boys, whereas the inverse association with high HbA1c was more evident in girls.ConclusionsOur data indicate that birthweight is significantly related to childhood cardiometabolic risk, independent of current BMI, and the associations exhibit race and gender-specific patterns.

Project description:BackgroundRefractive surgery research aims to optimally precategorize patients by their suitability for various types of surgery. Recent advances have led to the development of artificial intelligence-powered algorithms, including machine learning approaches, to assess risks and enhance workflow. Large language models (LLMs) like ChatGPT-4 (OpenAI LP) have emerged as potential general artificial intelligence tools that can assist across various disciplines, possibly including refractive surgery decision-making. However, their actual capabilities in precategorizing refractive surgery patients based on real-world parameters remain unexplored.ObjectiveThis exploratory study aimed to validate ChatGPT-4's capabilities in precategorizing refractive surgery patients based on commonly used clinical parameters. The goal was to assess whether ChatGPT-4's performance when categorizing batch inputs is comparable to those made by a refractive surgeon. A simple binary set of categories (patient suitable for laser refractive surgery or not) as well as a more detailed set were compared.MethodsData from 100 consecutive patients from a refractive clinic were anonymized and analyzed. Parameters included age, sex, manifest refraction, visual acuity, and various corneal measurements and indices from Scheimpflug imaging. This study compared ChatGPT-4's performance with a clinician's categorizations using Cohen κ coefficient, a chi-square test, a confusion matrix, accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve.ResultsA statistically significant noncoincidental accordance was found between ChatGPT-4 and the clinician's categorizations with a Cohen κ coefficient of 0.399 for 6 categories (95% CI 0.256-0.537) and 0.610 for binary categorization (95% CI 0.372-0.792). The model showed temporal instability and response variability, however. The chi-square test on 6 categories indicated an association between the 2 raters' distributions (χ²5=94.7, P<.001). Here, the accuracy was 0.68, precision 0.75, recall 0.68, and F1-score 0.70. For 2 categories, the accuracy was 0.88, precision 0.88, recall 0.88, F1-score 0.88, and area under the curve 0.79.ConclusionsThis study revealed that ChatGPT-4 exhibits potential as a precategorization tool in refractive surgery, showing promising agreement with clinician categorizations. However, its main limitations include, among others, dependency on solely one human rater, small sample size, the instability and variability of ChatGPT's (OpenAI LP) output between iterations and nontransparency of the underlying models. The results encourage further exploration into the application of LLMs like ChatGPT-4 in health care, particularly in decision-making processes that require understanding vast clinical data. Future research should focus on defining the model's accuracy with prompt and vignette standardization, detecting confounding factors, and comparing to other versions of ChatGPT-4 and other LLMs to pave the way for larger-scale validation and real-world implementation.

Dataset Information

Multiracial Children's and Adults' Categorizations of Multiracial Individuals.

Publications

Multiracial Children's and Adults' Categorizations of Multiracial Individuals.

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