San Diego State University
  • San Diego, California, United States
Recent publications
Background As the legalization of cannabis moves forward in many countries, it is important to highlight the potential harm that excessive use can cause on young consumers. Crafting effective policy interventions to reduce the harm stemming from excessive use requires an understanding of the attitudes and motivations of young consumers. Methods This article uses Q methodology to study four aspects of cannabis use among young adults from Mexico City’s metropolitan area: motivations for use, perceived consequences of use, reasons that would increase willingness to reduce consumption, and attitudes towards government regulation. A total of 110 cannabis users between 18 and 21 years old were recruited using chain-referral sampling. Using a Q methodology, we captured the relative importance that participants assigned to a series of statements and identified archetypal profiles of young adults who use cannabis for each of the four aspects mentioned above. Results The sample for this research study included 76 men and 34 women. The average age of participants was 20 years old, and the average age when cannabis consumption started was 15 years old. For each of the four Q-sort factor analyses, we identified 4 distinct factors based on explained variance and interpretability. The Q factor analysis indicated that attenuation of a negative affect (i.e., anxiety, stress) and relaxation were primary motivations for cannabis use. Understood consequences of cannabis use ranged across aspect-archetype, reflecting legal (i.e., interacting with law enforcement), financial, familial (i.e., disappointing family members), and educational performance concerns. Participants indicated that finding alternative relaxation strategies, receiving credible evidence of the health harms of cannabis use, increased financial burden of purchasing, and increased inaccessibility of cannabis products would motivate reductions in use. Across archetypes, participants indicated a willingness to comply with cannabis policies which are simple and easy to understand, which do not lead to discrimination or law enforcement involvement, and which provide for legal places to purchase and use safe (i.e., free of adulterants) cannabis products. Conclusions We posit that these archetypes could be useful to inform cannabis policy design. As the study reveals, participants’ cannabis use was primarily motivated by perceived improvements to mental health. Furthermore, participant responses indicated that they viewed cannabis use as a health matter, not a criminal one. Policies which aim to promote alternative mental health wellness and relaxation mechanisms, which aim to improve communication of potential health harms of cannabis, and which allow for the safe and legal purchase and use of cannabis may be effective in reducing cannabis-associated harms. Though our findings shed light on important aspects of cannabis users’ attitudes and perspectives, the sample size does not allow for a generalization of the findings and the drawing of conclusions about the population under scrutiny. Further research should consider the application of the Q methodology used in this article to a larger and more representative sample of cannabis users.
Objective The COVID-19 pandemic has forced many human subjects research to halt in-person activities and pivot to virtual engagement, including Focus Groups (FGs). We highlight learnings from our experience of hosting virtual FGs from our BEhavioral EConomics for Oral health iNnovation (BEECON) study focusing on oral hygiene behaviors among low-income, predominantly Hispanic families, including practical tips and potential pitfalls to avoid for researchers considering virtual engagement. Results There can be particular benefits to holding virtual sessions among minority parents of young children—to provide flexibility, comfort, and reduced logistical barriers for participation—while still facilitating friendly conversation with minimal distractions. However, extensive preparation is needed to ensure smooth execution and minimal distractions.
Background Policy documents like Vision and Change and the Next Generation Science Standards emphasize the importance of using constructed-response assessments to measure student learning, but little work has examined the extent to which administration conditions (e.g., participation incentives, end-of-course timing) bias inferences about learning using such instruments. This study investigates potential biases in the measurement of evolution understanding (one time point) and learning (pre-post) using a constructed-response instrument. Methods The constructed-response ACORNS instrument (Assessment of COntextual Reasoning about Natural Selection) was administered at the beginning of the semester, during the final exam, and at end of the semester to large samples of North American undergraduates (N = 488–1379, 68–96% participation rate). Three ACORNS scores were studied: number of evolutionary core concepts (CC), presence of evolutionary misconceptions (MIS), and presence of normative scientific reasoning across contexts (MODC). Hierarchical logistic and linear models (HLMs) were used to study the impact of participation incentives (regular credit vs. extra credit) and end-of-course timing (final exam vs. post-test) on inferences about evolution understanding (single time point) and learning (pre-post) derived from the three ACORNS scores. The analyses also explored whether results were generalizable across race/ethnicity and gender. Results Variation in participation incentives and end-of-course ACORNS administration timing did not meaningfully impact inferences about evolution understanding (i.e., interpretations of CC, MIS, and MODC magnitudes at a single time point); all comparisons were either insignificant or, if significant, considered to be small effect sizes. Furthermore, participation incentives and end-of-course timing did not meaningfully impact inferences about evolution learning (i.e., interpretations of CC, MIS, and MODC changes through time). These findings were consistent across race/ethnicity and gender groups. Conclusion Inferences about evolution understanding and learning derived from ACORNS scores were in most cases robust to variations in participation incentives and end-of-course timing, suggesting that educators may have some flexibility in terms of when and how they deploy the ACORNS instrument.
Additive manufacturing and data analytics are independently flourishing research areas, where the latter can be leveraged to gain a great insight into the former. In this paper, the mechanical responses of additively manufactured samples using vat polymerization process with different weight ratios of magnetic microparticles were used to develop, train, and validate a neural network model. Samples with six different compositions, ranging from neat photopolymer to a composite of photopolymer with 4 wt.% of magnetic particles, were manufactured and mechanically tested at quasi-static strain rate and ambient environmental conditions. The experimental data were also synthesized using a data-driven approach based on shape-preserving piecewise interpolations while leveraging the concept of simple micromechanics rule of mixture. The overarching objective is to forecast the mechanical behavior of new compositions to eliminate or reduce the need for exhaustive post-manufacturing testing, resulting in an accelerated product development cycle. The ML model predictions were found to be in excellent agreement with the experimental data for prognostication of the mechanical behavior of physically tested samples with near-unity correlation coefficients. Furthermore, the ML model performed reasonably well in predicting the mechanical response of untested, newly formulated compositions of photopolymers and magnetic particles. On the other hand, the data-driven approach predictions suffered from processing artifacts, demonstrating the superiority of ML algorithms in handling this type of data. Overall, this analysis approach holds great potential in advancing the prospects of additive manufacturing and model-less mechanics of material analyses. A byproduct of the ML approach is using the results for quality assurance, accelerating the acceptance of additively manufactured parts into industrial deployments.
This study is the first to explore the impact of the January 6, 2021 Capitol riot on risk avoidance behavior and the spread of COVID-19. First, using anonymized smartphone data from SafeGraph, Inc., and an event-study approach, we document a substantial increase on January 6 in non-resident smartphone pings at the sites of the protest: the Ellipse, the National Mall, and the US Capitol Building. Then, using data from the same source and a synthetic control approach, we find that the Capitol riot led to an increase in stay-at-home behavior among District of Columbia residents, consistent with risk avoidance behavior and post-riot policies designed to limit large in-person gatherings. Finally, while we find no evidence that the Capitol riot substantially increased the spread of COVID-19 in the District of Columbia, we do find that counties with the highest inflows of out-of-town protesters experienced a 0.004 to 0.010 increase in the rate of daily cumulative COVID-19 case growth during the month following the event. These findings are exacerbated in counties without COVID-19 mitigation policies in place. Supplementary information: The online version contains supplementary material available at 10.1007/s00148-022-00914-0.
A Maximum Likelihood recursive state estimator is derived for non-linear state–space models. The estimator iteratively combines a particle filter to generate the predicted/filtered state densities and the Expectation Maximization algorithm to compute the maximum likelihood filtered state estimate. Algorithms for maximum likelihood state filtering, prediction and smoothing are derived. The convergence properties of these algorithms, which are inherited from the Expectation Maximization algorithm and the particle filter, are examined in two examples. For nonlinear state–space systems with linear measurements and additive Gaussian noises, it is shown that the filtering and prediction algorithms reduce to gradient-free optimization in a form of a fixed-point iteration. It is also shown that, with randomized reinitialization, which is feasible because of the simplicity of the algorithm, these methods are able to converge to the Maximum Likelihood Estimate (MLE) of multimodal, truncated and skewed densities, as well as those of disjoint support.
Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.
Researchers analyzed quantitative data from the Education Longitudinal Study (2002–2004) to investigate the relationship between the highest mathematics course taken and the achievement of 12th-grade students minoritized by their racial-ethnic and language backgrounds in urban schools. Employing hierarchical linear models, researchers analyzed the effects of student linguistic minority (LM) status, English-language proficiency, and school urbanicity on mathematics achievement. Findings suggest an interdependent relationship between (a) students’ English-language and racial-ethnic backgrounds, (b) college-preparatory mathematics course-taking, (c) the urban school context, and (d) mathematics achievement. Researchers suggest promising education policies and pedagogical practices for improving LMs’ inequitable achievement outcomes by maximizing students’ opportunities-to-learn in college preparatory courses and facilitating the academic language of mathematics.
This study examines changes in labor supply, income, and time allocation during the COVID-19 pandemic in Mexico. Using an event-study design, we show that the COVID-19 recession had severe negative consequences for Mexican households. In the first month of the pandemic, employment declined by 17 percentage points. Men recovered their employment faster than women, where men’s employment approaches original levels by 2021Q2. Women, on the other hand, experienced persistent employment losses. Within-household, men also increased their time spent on household chores while neither gender (persistently) increased their time caring for others. Instead, children reduced their time spent on schoolwork by 25%.
Forest treatments reduce wildfire risk and can promote the vigor and production of remaining trees, but they are also a disturbance. Understanding the type, timing, and longevity of tree response to treatment, as well as the potential for interactive effects of treatments and drought, could help managers plan and evaluate forest management practices. Environmental drivers, biological modifiers, and tree capacity to respond to prior disturbances were concurrently tested to predict ponderosa pine basal area increment (BAI) in a lowland and upland dry pine forest in south central Oregon, USA. Environmental drivers included current year and lags or running averages of a drought index, SPEI, and the sum or count of growing degree days >0°C or 10°C. Biological modifiers of environmental drivers considered pre-treatment response to disturbance, tree vigor, and tree-to-tree competition. A model was developed to predict BAI in both topographic positions for applicability to the landscape level, and then was used to test for specific differences in BAI between paired forest treatments differing by one treatment. Forest treatments tested included no management (NM), undercut and even spacing harvest (HE), prescribed fire (Rx), and their combinations. HE significantly increased BAI shortly after treatment. Post-harvest, one or two Rx did not provide additional BAI benefits, nor in the absence of HE, did 2Rx vs. 1Rx treatment. The 1Rx treatment was imposed between multi-year droughts; BAI significantly increased after the treatment and was resistant to droughts. Upland trees were affected by a single year of drought; lowland trees responded only after sequential drought years. A single treatment, HE or 1Rx appeared to be as effective as multiple or mixed treatments in improving BAI in dry pine forest stands. HE appeared to generate the largest effect. Timing of forest treatments relative to site water balance may affect short term (decadal) wood production.
Children often display non-adult-like behaviors when reasoning with quantifiers and logical connectives in natural language. A classic example of this is the symmetrical interpretation of universally quantified statements like “Every girl is riding an elephant”, which children often reject as false when they are used to describe a scene with, e.g., three girls each riding an elephant and a fourth elephant without a rider. We present evidence that children's understanding of these sentences is not attributable to syntactic, semantic, or general processing limitations. Instead, in two experiments, we argue that children's behavior stems primarily from difficulty in correctly identifying the speaker's intended “question under discussion”, and that when this question is made contextually unambiguous, children's judgments are almost completely adultlike.
This qualitative study aimed to understand how migration experiences shape im/migrant women's needs, desire for, and expectations of healthcare in the British Columbia (BC), Canada context. Interviews with 33 im/migrant women (December 2018–January 2020) highlighted that traumatic experiences across migration increased healthcare needs; insufficient prior health system information contributed to poor experiences; and comparative healthcare experiences across places shaped future healthcare expectations. We use the BC setting to demonstrate the need to abide by global commitments to protect people during migration, train providers in trauma-informed care, develop health assessments that center migration journeys, and appropriately fund im/migrant-serving community organizations.
It is common for psychology studies to rely solely on linear correlation (r) or similar statistics and not include other measures of association (such as relative risk, which examines differences in the number of people affected). For example, the association between smoking and lung cancer (r = 0.06) could be dismissed as “small” if only linear r is examined, even though 30 times more smokers than non-smokers get lung cancer. Many studies concluding that associations between technology use and well-being as too small to be of practical importance relied solely on linear r. We show that, across five datasets, “small” correlations between technology use and mental health exist alongside practically important risk associations. As there are several valid types of association, and characterizing an association based on a single type of a measure – such as linear r or r² – can be misleading.
Background: Drug overdoses are the leading cause of injury death in the United States with an estimated 105,752 individuals dying from an overdose in the United States in a 12-month period ending October 2021. Given that people who have opioid use disorder (OUD) are at an increased risk of death, it is crucial to assess risk factors associated with opioid overdose to improve interventions. Objectives: We examine factors associated with non-fatal overdose among a suburban/exurban population with OUD in Southern California. Methods: Participants were recruited by convenience sampling (n = 355) and were interviewed between November 2017 to August 2018. Participants were eligible for the study if they had a history of pharmaceutical opioid use. Results: A total of 198 (55.8%) participants reported at least one overdose in their lifetime. A total of 229 participants identified as male, 124 identified as female, and 2 identified as non-binary. When controlling for demographic factors, non-oral opioid administration at first opioid use (AOR 2.82, 95% CI 1.52-5.22), having a history of methadone detoxification, (AOR 2.23, 95% CI 1.27-3.91), history of buprenorphine detoxification (AOR 1.77, 95% CI 1.02-3.07), and history of 12 step attendance (AOR 1.89, 95% CI 1.12-3.20) were found to be independently and positively associated with lifetime opioid overdose. Conclusions: Detoxification with buprenorphine and methadone was found to be associated with having a non-fatal opioid overdose. Buprenorphine and methadone should not be prescribed as a detoxification medication as long-term use of medication for OUD results in better outcomes than medication that is used short-term.
Objective: Despite significant work in African and Hispanic American populations, little information is available regarding performance of Japanese Americans on neuropsychological tests. The aim of this study was to examine the effects of dominant language and acculturation levels on the performance of Japanese Americans on selected neurocognitive tests. Method: Based on their self-identified dominant language, 48 English-dominant speaking (ES) Japanese Americans (Mage = 64.48, SD = 10.52) and 52 Japanese-dominant speaking (JS) Japanese Americans (Mage = 60.17, SD = 11.15) were assessed on a neurocognitive battery. Results: Significant differences in test performance were observed between the groups, with the JS group performing poorer on the measures of naming ability, verbal and olfactory learning/memory, and language, compared to the ES group. Levels of acculturation explained that group difference. The Brief Visuospatial Memory Test-Revised (BVMT-R) showed no group differences, suggesting lack of language proficiency and acculturation biases in this ethnic sample. Within the JS group, self-reported English proficiency and years of education obtained in Japan explained variance in addition to age, education, and gender, in performance on the Boston Naming Test and the Letter Fluency Test, respectively. Conclusions: The present study highlights the need for culturally sensitive evaluation in the neuropsychological assessment of this population. The variability in backgrounds contributed to the variability in performance between and within groups. Factors in addition to age and education, including the effects of primary language and acculturation, warrant consideration when evaluating the neuropsychological performance of Japanese Americans in research and clinical settings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Distribution of Earth’s biomes is structured by the match between climate and plant traits, which in turn shape associated communities and ecosystem processes and services. However, that climate–trait match can be disrupted by historical events, with lasting ecosystem impacts. As Earth’s environment changes faster than at any time in human history, critical questions are whether and how organismal traits and ecosystems can adjust to altered conditions. We quantified the relative importance of current environmental forcing versus evolutionary history in shaping the growth form (stature and biomass) and associated community of eelgrass ( Zostera marina ), a widespread foundation plant of marine ecosystems along Northern Hemisphere coastlines, which experienced major shifts in distribution and genetic composition during the Pleistocene. We found that eelgrass stature and biomass retain a legacy of the Pleistocene colonization of the Atlantic from the ancestral Pacific range and of more recent within-basin bottlenecks and genetic differentiation. This evolutionary legacy in turn influences the biomass of associated algae and invertebrates that fuel coastal food webs, with effects comparable to or stronger than effects of current environmental forcing. Such historical lags in phenotypic acclimatization may constrain ecosystem adjustments to rapid anthropogenic climate change, thus altering predictions about the future functioning of ecosystems.
Objective: To identify (1) university educators' perceptions of academic adjustments (AA), and (2) if teaching experience correlated with AA perceptions following concussion. Participants: Two hundred twenty educators. Methods: University educators were invited to complete a survey containing four subsections; this manuscript focuses on AA following concussion. Objective 1 was descriptive; we conducted spearman's rho correlations between years of teaching experience and AA perceptions to address objective 2. Results: Educators were moderately familiar with AA but were not confident in their knowledge about AA following concussion. Participants who provided AA following concussion most often allowed excused absences and extra time for exams/assignments. There were no significant relationships between teaching experience and perceptions of AA. Conclusions: University educators largely feel unprepared to provide or recommend AA following concussion but had favorable AA perceptions following concussion. Standardized policies or referral sites within the university system may be warranted to improve post-concussion AA.
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Stanley R. Maloy
  • Research and Graduate Affairs
fred joel harris
  • Department of Electrical and Computer Engineering
Tracy Love
  • School of Speech, Language, and Hearing Sciences
Carol Macera
  • Graduate School of Public Health
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