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Open Access Publications from the University of California

Scholarly Works (47 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 46 (2024)
Language represents a framework used to organize the things we experience. Redundant linguistic category labels facilitate category learning at a faster rate than category learning without labels (Luypan et al., 2007) suggesting language is also meaningfully involved in forming new categories. However, labels are not exclusively advantageous. Brojde et al., (2011) demonstrates that labels can be detrimental to category learning dependent on attending to historically agnostic dimensions over historically diagnostic ones (i.e., learning texture-based categories while ignoring shape). To separate historical experience from novel category learning, we task participants with classifying stimuli based on perceptual dimensions with less historical precedence as diagnostic cues for categorizing objects in everyday life (i.e., orientation and spatial frequency). Our results reveal a labeling advantage as well as slower overall learning in the orientation condition compared to spatial frequency-based learning. We discuss implications involving the historical use of these dimensions and the relationship between diagnostic and non-diagnostic dimensions.
    Cover page: Labels aid in the more difficult of two category learning tasks: Implications for the relative diagnosticity of perceptual dimensions in selective attention tasks
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2020)

    Background: Every child deserves an equal chance at good health and cognitive development, but growth failure from undernutrition and disease rob many poor children of this opportunity in the first 2 years of their life. Child malnutrition kills roughly three million children every year, and many more suffer long-term health consequences from impaired physical growth and cognitive development, especially if malnutrition occurs during the first 1000 days of life. Wasting and stunting, two markers of child malnutrition, are strongly associated with increased mortality and decreased cognitive development, and are still highly prevalent in developing countries, even though their reduction are targets of the World Health Organization Millennium Development Goals. Trials on nutritional interventions have had null results or not clinically meaningful effect sizes. Most existing research on exposures associated with stunting and wasting rely on small cohorts or large cross-sectional surveys focused on stunting and wasting prevalence. Pooled analyses of data from many longitudinal studies offer unique opportunities to identify novel patterns in child undernutrition across combinations of time, space, and human characteristics, which is not possible in smaller datasets. The objective of this research was to answer the following questions by pooling information across multiple cohorts: when and where growth failure occurs, what characteristics best identify children at risk, and how early growth failure affects later growth and mortality. Answering these questions may help inform generalizable rules for child monitoring and develop preventative intervention to stop the initial onset of wasting and stunting.

    Methods: In this dissertation, I conduct an individual-participant-data meta-analysis of 35 different longitudinal cohorts and trials from 15 different countries to identify patterns in wasting incidence and recovery across child ages, across regions, and across seasons. I use machine learning and targeted learning methods to identify key child, parental, and household characteristics associated with wasting and stunting, adjusting for potential confounders. In addition to estimating relative risks of wasting between levels of characteristics, I estimate more policy-relevant population attributable fractions and a novel variable importance metric. In the first chapter I introduce the motivation for the analyses and give an overview of individual participant data meta-analysis methods and advantages. In the second chapter I examine longitudinal patterns in wasting incidence, recovery, seasonality, and concurrence with stunting by using a subset of 18 monthly-measured cohorts, and I compare differences in the epidemiology of wasting across regions. In the third chapter I estimate the relative importance of the causes of early life growth failure using targeted learning methodologies. Using machine learning to flexibly adjust for measured confounders, I estimate and rank order associations between 36 child, parental, and household characteristics and measures of growth failure in 31 cohorts and trials. In the fourth chapter I estimate the associations between early growth failure and subsequent adverse outcomes, including mortality. In the fifth chapter I discuss key subject-area advancements in knowledge from these analyses and offer the public health implications.

    Significance: This dissertation is currently the largest scale individual-participant data analysis of longitudinal patterns in and causes of child malnutrition in the first 1000 days of life. Using longitudinal, high quality anthropometry measurements, I examined patterns in wasting onset, recovery, seasonality, and concurrence with stunting not possible from national health surveys or accurate from small cohorts. The data were large in scope as well as scale, with data from 35 separate research sites (94,019 children and 645,869 total measurements), 18 with monthly measured cohorts (10,854 children and 187,215 anthropometry measurements). I was able to examine effect modification of wasting incidence by region and season. The results from chapter two found a high prevalence of wasting incidence at birth and the first three months of life, especially in South Asia. This is earlier than the paradigm in the nutrition field that most wasting episodes occur in children older than six months, after the cessation of exclusive breastfeeding and the start of crawling behavior. My findings suggest that interventions to prevent child acute malnutrition may have the largest impact if delivered earlier, including maternal interventions during pregnancy and early lactation and child interventions through the first months of life. Development of maternally focused, prenatal interventions represents an important opportunity to prevent child growth failure in lower-or-middle income countries (LMICs), especially as I found that early growth failure had a strong relationship with mortality. Future research into potential tradeoffs in prioritizing exclusive breast feeding versus the prevention or treatment of wasting in the first 6 months is needed. I also found high seasonality in wasting in synchrony with rainfall patterns, possibly due to seasonality in food availability and infectious diseases, indicating the timing of intervention delivery should target seasonal peaks in wasting. The strongest determinants of child growth failure were maternal anthropometry and child birth size. Future research should use the key determinants identified to find the optimal subgroups of children who would benefit most from postnatal interventions to start and reduce the substantial global health burden of child growth failure.

      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2021)
      Shifting the public towards plant-based diets is critical for achieving environmental and public health outcomes. Increasingly news articles and organizations have begun using the saliency of the COVID-19 crisis to highlight the link between animal agriculture, pandemic risks, and other widespread public health threats. Yet, little is known about the effectiveness of this messaging strategy for motivating dietary change. We conducted a randomized trial with an online sample to examine the impact of: (1) a message that uses the saliency of the COVID-19 pandemic to highlight the risk of disease transmission from factory farms, and (2) a message that uses the saliency of the COVID-19 pandemic to highlight the threat to worker's health created by factory farms. We examine whether these messages are more effective at changing beliefs about and behavioral intentions towards plant-based eating, as compared to more traditional messages that highlight the environmental, personal health, or animal welfare implications of factory farmed meat consumption. We find that all messages differentially influenced beliefs about the various negative consequences of meat consumption. However, these altered beliefs did not differentially motivate changes in respondents' intentions to reduce meat consumption and choose plant-based alternatives. This was possibly due to the numerous other barriers to behavior change identified in qualitative survey responses, such as cost, taste, and social factors. We did find that messages that highlight the personal health benefits of reduced meat consumption were more effective at increasing public trust in the message deliverer. Our results suggest that highlighting personal health benefits in messaging and addressing the additional identified barriers to behavior change may be critical for building trust and shifting the public towards plant-based diets.
        Cover page: The effectiveness of COVID-related message framing on public beliefs and behaviors related to plant-based diets
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2020)
        Failures to reproduce research findings across scientific disciplines from psychology to physics have garnered increasing attention in recent years. External replication of published findings by outside investigators has emerged as a method to detect errors and bias in the published literature. However, some studies influence policy and practice before external replication efforts can confirm or challenge the original contributions. Uncovering and resolving errors before publication would increase the efficiency of the scientific process by increasing the accuracy of published evidence. Here we summarize the rationale and best practices for internal replication, a process in which multiple independent data analysts replicate an analysis and correct errors prior to publication. We explain how internal replication should reduce errors and bias that arise during data analyses and argue that it will be most effective when coupled with pre-specified hypotheses and analysis plans and performed with data analysts masked to experimental group assignments. By improving the reproducibility of published evidence, internal replication should contribute to more rapid scientific advances.
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2023)
          BACKGROUND: Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. METHODS: The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. RESULTS: In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. CONCLUSION: These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.
            Cover page: An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2020)
            Failures to reproduce research findings across scientific disciplines from psychology to physics have garnered increasing attention in recent years. External replication of published findings by outside investigators has emerged as a method to detect errors and bias in the published literature. However, some studies influence policy and practice before external replication efforts can confirm or challenge the original contributions. Uncovering and resolving errors before publication would increase the efficiency of the scientific process by increasing the accuracy of published evidence. Here we summarize the rationale and best practices for internal replication, a process in which multiple independent data analysts replicate an analysis and correct errors prior to publication. We explain how internal replication should reduce errors and bias that arise during data analyses and argue that it will be most effective when coupled with pre-specified hypotheses and analysis plans and performed with data analysts masked to experimental group assignments. By improving the reproducibility of published evidence, internal replication should contribute to more rapid scientific advances.
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2020)
              Failures to reproduce research findings across scientific disciplines from psychology to physics have garnered increasing attention in recent years. External replication of published findings by outside investigators has emerged as a method to detect errors and bias in the published literature. However, some studies influence policy and practice before external replication efforts can confirm or challenge the original contributions. Uncovering and resolving errors before publication would increase the efficiency of the scientific process by increasing the accuracy of published evidence. Here we summarize the rationale and best practices for internal replication, a process in which multiple independent data analysts replicate an analysis and correct errors prior to publication. We explain how internal replication should reduce errors and bias that arise during data analyses and argue that it will be most effective when coupled with pre-specified hypotheses and analysis plans and performed with data analysts masked to experimental group assignments. By improving the reproducibility of published evidence, internal replication should contribute to more rapid scientific advances.
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2024)
                OBJECTIVES: (1) Assess and compare the perceptions of pet owners and veterinary professionals pertaining to the extent of veterinary workforce and access to care challenges in 2023 in Colorado, and (2) Assess what programs, policies, and resources veterinary professionals and pet owners believe would be most effective at addressing access to care and workforce challenges in Colorado. SAMPLE: 736 veterinarians, veterinary technicians, or practice/owner manager (veterinary professionals) in Colorado. A total of 1,209 pet owners (919 from an online survey and 290 from in-person surveying). PROCEDURES: Distribution of an online anonymous survey to veterinary professionals in Colorado. Pet owners were surveyed both online and in-person at pet pantry or shelter events. RESULTS: Veterinary professionals reported significant workforce challenges, including having to frequently divert clients, clients forced to decline medical care or having to euthanize their pets due to cost. Veterinary professionals were especially supportive of policy efforts to enhance recruitment and retention of technicians, including through mechanisms such as clarifying their scope of practice, loan repayment programs, and enhancing career pathways. Colorado pet owners responses pertaining to the scope of access to care challenges were similar to prior national research. Pet owners reported particularly needing low-cost emergency clinics in their community as well as resources to reduce the cost of care. Pet owners were generally supportive of expanding veterinary care access through telemedicine; indicating they would feel comfortable seeing a veterinarian via telemedicine, even for the first time, and that expanded use of telemedicine would increase their ability to obtain care. CONCLUSIONS AND CLINICAL RELEVANCE: Colorado pet owners and veterinary professionals both identified numerous access to care challenges as well as indicated support for the development of several potential initiatives to address the problem. Low-cost clinics that provide sick and emergency care was the resource rated as being most helpful among pet owners. Further exploration of grants, voucher programs, expansion of telemedicine, increased utilization and title protection for CVTs, and the creation of the veterinary professional associate position are all initiatives that were noted to be worthy of further exploration.
                  Cover page: Veterinary and pet owner perspectives on addressing access to veterinary care and workforce challenges.
                  • Article
                  • Peer Reviewed
                  UC San Francisco Previously Published Works (2020)
                  We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relationships exist between covariates and outcomes, while our approach provides an alternative that is useful in situations where complex relationships may be present. We utilize machine learning to detect nonlinear relationships and covariate interactions and propose a measure of association that captures these relationships. A hypothesis test about the proposed associative measure can be used to test the strong null hypothesis of no association between a set of variables and a multivariate outcome. Simulations demonstrate that this hypothesis test has greater power than existing methods against alternatives where covariates have nonlinear relationships with outcomes. We additionally propose measures of variable importance for groups of variables, which summarize each groups' association with the outcome. We demonstrate our methodology using data from a birth cohort study on childhood health and nutrition in the Philippines.
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2022)
                    Childhood wasting and stunting affect large numbers of children globally. Both are important risk factors for illness and death yet, despite the fact that these conditions can share common risk factors and are often seen in the same child, they are commonly portrayed as relatively distinct manifestations of undernutrition. In 2014, the Wasting and Stunting project was launched by the Emergency Nutrition Network. Its aim was to better understand the complex relationship and associations between wasting and stunting and examine whether current separations that were apparent in approaches to policy, financing, and programs were justified or useful. Based on the project's work, this article aims to bring a wasting and stunting lens to how research is designed and financed in order for the nutrition community to better understand, prevent, and treat child undernutrition. Discussion of lessons learnt focuses on the synergy and temporal relationships between children's weight loss and linear growth faltering, the proximal and distal factors that drive diverse forms of undernutrition, and identifying and targeting people most at risk. Supporting progress in all these areas requires research collaborations across interest groups that highlight the value of research that moves beyond a focus on single forms of undernutrition, and ensures that there is equal attention given to wasting as to other forms of malnutrition, wherever it is present.
                      Cover page: How Can Nutrition Research Better Reflect the Relationship Between Wasting and Stunting in Children? Learnings from the Wasting and Stunting Project
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