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Occupational therapy practitioners address pain management across settings, populations, and the lifespan. Occupational therapy practitioners offer unique contributions to pain management through biopsychosocial approaches, targeting supported self-management and occupational engagement. Comprehensive pain education is necessary to prepare entry-level occupational therapy practitioners to address pain in practice, yet no standards exist in entry-level occupational therapy education. This survey describes the status of pain-related education in entry-level occupational therapy programs across the United States. A total of 41 entry-level occupational therapy programs completed the survey. Total instructional time for pain content was reported to be an average of 9.68 hours, ranging from 1 to 30 hours, indicating significant variability. Average lecture (4.88 hours) and lab-based (5.05 hours) time devoted to pain content were similar. More time was devoted to teaching pain interventions (4.91 hours) than pain assessments (2.82 hours). Most programs integrated pain content throughout the curriculum (73%). One program utilized a standardized pain curriculum (International Association for the Study of Pain Occupational Therapy Curriculum). Open-ended questions revealed a breadth of evidence-based resources, pain assessments, and interventions covering all domains of the biopsychosocial model of pain. The majority of faculty (61%) felt their current amount of pain education was insufficient, citing the need for increased time and educational activities for pain education across all program years. Given the essential and unique role of occupational therapy practitioners in pain management, improving pain education and practitioner competence is an important consideration across entry-level occupational therapy education. Further discussion is needed regarding educational standards and entry-level occupational therapy curricula relating to pain management.

Clearly defined professional standards result in better prepared professionals who positively impact outcomes for children and families by ensuring an effective workforce. This article describes the evolution of early intervention early/childhood special education preparation standards from the on-the-job competencies of the 1960s to the historic 2020 Initial Practice-Based Professional Standards for Early Interventionists/Early Childhood Special Educators (EI/ ECSE Standards), in concert with the evolution of the profession itself. Influencing factors include (a) changes in federal legislation and policy, (b) ages and characteristics of children served, (c) growing knowledge of effective practices, (d) collaboration with other disciplines, and (e) ongoing advocacy for EI/ ECSE as a profession. The article concludes with a vision for using the EI/ECSE Standards to guide the future local, state, and national agenda of the profession around preservice preparation and accreditation, professional development, state and federal policy, cross-disciplinary collaboration, and standards-informed research.

The global pandemic highlighted the need for diverse faculty development partners to ensure student and faculty learning was supported, particularly in intensive modes of educational delivery. Our paper presents an institutional case study of how educational technology, in collaboration with the Center for Teaching and Learning and subject matter experts, served as untapped providers of faculty development. We detail the decision to shift to an intensive 7-week module system rather than our traditional 15-week semester in response to COVID-19. Although challenging for both faculty and students, this shift in educational delivery facilitated innovative approaches to faculty and student learning that are present on our campus today. This institutional case study highlights the role that capacity-building plays in capability development and professional learning for faculty and students alike to support effective teaching practice across diverse delivery modes.

Much of the research on science, technology, engineering, and mathematics (STEM) students' motivation measures the relationship between student motivation and academic outcomes, focusing on the student's mindset. Our mixed-methods research takes a different approach and considers the relationship between student motivation and instructional practices. Teaching practices and student motivation were analyzed simultaneously in undergraduate Biology classes using a self-determination theory-based survey to measure students' motivation during courses that were observed using the Classroom Observation Protocol for Undergraduate STEM (COPUS), and observation notes were collected to document instructor and student behaviors. Quantitative data were used to differentiate students' motivational levels, and qualitative data were collected to describe how instructors use specific teaching practices. The results provide a lens into how students' intrinsic motivation varies alongside the instructional practices and interactions in these classes. We found a correlation between higher levels of student motivation in interactive lectures and student-centered teaching profiles. This study highlights how the same practice can be implemented by multiple instructors with varying student motivation scores, pointing out the importance of fidelity to evidence-based instructional practice methods. The results of this study are discussed in the context of published empirical studies examining evidence-based instructional practices that are conceptually supportive of autonomy, competence, and relatedness. Active learning practices observed in this study correlated to positive learning outcomes are discussed and may serve as a guide for instructors interested in implementing specific active learning practices. Recommendations for instructors and departments that are interested in flexible methods to monitor progress toward active learning practices in biology and other STEM disciplines by combining the COPUS and self-determination survey results are presented.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that includes social-communication deficits and repetitive and stereotypical behaviours (APA 2022). Neurobiological methods of studying ASD are a promising methodology for identifying ASD biomarkers. Mu rhythms (Mu) have the potential to shed light on the socialisation deficits that characterise ASD; however, Mu/ASD studies thus far have yielded inconsistent results. This review examines the existing Mu/ASD studies to determine where this variability lies to elucidate potential factors that can be addressed in future studies.

Assessing students' (mis)conceptions is a challenging task for teachers as well as for researchers. While individual assessment, for example through interviews, can provide deep insights into students' thinking, this is very time-consuming and therefore not feasible for whole classes or even larger settings. For those settings, automatically evaluated multiple-choice (MC) items could be a solution. However, it is a challenge to design those items and to adapt them for other countries in a way that they adequately reveal students' (mis)conceptions. In this article, we investigate the question whether it is valid to use a German adaption of a multiple-choice test developed in Australia for formative assessment of the "letter-as-object" misconception in Germany. For this, first semi-structured interviews with five German Year 8 students were conducted, and second, 616 students were asked for short written explanations. These data were analysed with regards to the students' (mis)conceptions and compared with their automatic online diagnosis. In general, a high concordance between online SMART test results and students' explanations was observed, confirming that useful diagnoses of student misconceptions can be obtained from such a short well-designed MC test.

Engaging students in the reading process is challenging when they are unable to connect to texts. It is important to provide inclusive and diverse texts (IDTs) in the language arts curriculum. To promote a positive reading experience, all students need to read IDTs with non-stereotypical depictions of girls, women, people of Color, and more. This is challenging when obstacles such as limited resources, a lack of teacher preparation to meet challenges, and stakeholder opposition to "non-traditional" literature may hinder educators' efforts to include IDTs. Using effective instructional strategies, increasing home and school literacy connections, and providing focused teacher training can help overcome these obstacles. Literary texts that reflect multiple identities will promote a more equitable representation of all students within the classroom community. This paper discusses possible strategies and approaches for including and engaging with IDTs and resources educators can use to address instructional challenges and find high-quality texts.

As mental health concerns coupled with inadequate supports have been described as reaching crisis proportions in autistic people, there is a pressing need for accessible and effective support systems. While self-compassion has shown various benefits in the general population, its application in supporting autistic individuals remains limited. This study investigated the experiences of 39 autistic adults who participated in an autism-specific online self-compassion program. The program included a series of five modules which incorporated psychoeducation, meditation, and self-reflective exercises that were completed over a 5-week period. Qualitative data was collected through weekly check-ins and a post-program survey, which underwent thematic analysis through the lens of an autistic researcher resulting in four key themes: the positive impact of self-compassion, challenges faced during the program, recognizing self-compassion as a journey, and the value of program adaptations. These findings shed light on the experiences of autistic adults engaging with self-compassion interventions, highlighting both the benefits and barriers they encountered. The study underscores the importance of developing tailored interventions that consider the unique needs and perspectives of autistic individuals to promote improved mental health outcomes and foster inclusivity.

In Victoria, Australia, senior secondary mathematics students are expected to use technology and thus need to make decisions about using pen-and-paper (P&P) or technology when solving mathematics problems. The predominant technology is a Computer Algebra System (CAS). This study investigated the beliefs about CAS held by twelve Year 11 students as they learnt to use CAS and whether these beliefs were stable over time. These students held a range of beliefs related to the usefulness of CAS, speed of CAS compared to P&P, whether CAS is proper mathematics, choice of CAS or P&P, ease of use, the correctness of answers and solving problems in Mathematical Methods (i.e. the mathematics subject studied). Beliefs are often described as being stable (e.g. McLeod, 1992), but some researchers stress stability needs to be determined empirically rather than being seen as a characteristic of beliefs (e.g. Liljedahl et al., 2012). For this sample of students, stability (rather than instability) is a feature of students' beliefs about CAS.

Although aqueous microdroplets have been shown to exhibit enhanced chemical reactivity compared to bulk solutions, mechanisms for these enhancements are not completely understood. Here we combine experimental measurements and kinetic modeling to show the strong coupling of interfacial reactions and gas/droplet partitioning in the condensation reaction of pyruvic acid (PA) to yield zymonic acid (ZA) in acidic aqueous microdroplets. Experimental analysis of single microdroplets reveals the substantial influence of evaporation of PA and partitioning of water on the size-, relative humidity (RH)- and temperature-dependent sigmoidal reaction kinetics for the condensation reaction. A newly developed diffusion-reaction-partitioning model is used to simulate the complex kinetics observed in the microdroplets. The model can quantitatively predict the size and compositional changes as the reaction proceeds under different environmental conditions, and provides insights into how microdroplet reactivity is controlled by coupled interfacial reactions and the gas-phase partitioning of PA and water. Importantly, the kinetic model best fits the data when an autocatalytic step is included in the mechanism, i.e. a reaction step where the product, ZA, catalyzes the interfacial condensation reaction. Overall, the dynamic nature of aqueous microdroplet chemistry and the coupling of interfacial chemistry with gas-phase partitioning are demonstrated. Furthermore, autocatalysis of small organic molecules at the air-water interface for aqueous microdroplets, shown here for the first time, has implications for several fields including prebiotic chemistry, atmospheric chemistry and chemical synthesis.

Variable wind speeds over the ocean can have a significant impact on the formation mechanism and physical-chemical properties of sea spray aerosols (SSA), which in turn influence their climate-relevant impacts. Herein, for the first time, we investigate the effects of wind speed on size-dependent morphology and composition of individual nascent SSA generated from wind-wave interactions of natural seawater within a wind-wave channel as a function of size and their particle-to-particle variability. Filter-based thermal optical analysis, atomic force microscopy (AFM), AFM infrared spectroscopy (AFM-IR), and scanning electron microscopy (SEM) were employed in this regard. This study focuses on SSA with sizes within 0.04-1.8 μm generated at two wind speeds: 10 m/s, representing a wind lull scenario over the ocean, and 19 m/s, indicative of the wind speeds encountered in stormy conditions. Filter-based measurements revealed a reduction of the organic mass fraction as the wind speed increases. AFM imaging at 20% relative humidity of individual SSA identified six main morphologies: prism-like, rounded, core-shell, rod, rod inclusion core-shell, and aggregates. At 10 m/s, most SSA were rounded, while at 19 m/s, core-shells became predominant. Based on AFM-IR, rounded SSA at both wind speeds had similar composition, mainly composed of aliphatic and oxygenated species, whereas the shells of core-shells displayed more oxygenated organics at 19 m/s and more aliphatic organics at 10 m/s. Collectively, our observations can be attributed to the disruption of the sea surface microlayer film structure at higher wind speeds. The findings reveal a significant impact of wind speed on morphology and composition of SSA, which should be accounted for accurate assessment of their climate effects.

Solid-water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real-world behavior. This is because these models use fixed parameters derived by averaging across a wide physicochemical range observed at the molecular scale. Recent studies have revealed the stochastic nature of molecular-level surface sites that define a variety of reaction mechanisms, rates, and products even across a single surface. To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions rather than with discrete constant values derived by averaging across a heterogeneous surface. This conceptual shift in continuum-scale modeling requires exponentially rising computational power. By incorporating our molecular-scale understanding of solid-water interfaces into continuum-scale models we can pave the way for next generation critical technologies and novel environmental solutions.

Nitrous acid (HONO) is a key molecule in the reactive nitrogen cycle. However, sources and sinks for HONO are not fully understood. Particulate nitrate photochemistry has been suggested to play a role in the formation of HONO in the marine boundary layer (MBL). Here we investigate the impact of marine relevant organic compounds on HONO formation from aqueous nitrate photochemistry. In particular, steady-state, gas-phase HONO yields were measured from irradiated nitrate solutions at low pH containing marine-dissolved organic matter (m-DOM). m-DOM induces a nonlinear increase in HONO yield across all concentrations compared to that for pure nitrate solutions, with rates of HONO formation increasing by up to 3-fold when m-DOM is present. Furthermore, to understand the potential synergistic effects that may occur within complex samples such as m-DOM, mixtures of chromophoric (light-absorbing) and aliphatic (non-light-absorbing) molecular proxies were utilized. In particular, mixtures of 4-benzoylbenzoic acid (4-BBA) and ethylene glycol (EG) in acidic aqueous solutions containing nitrate showed more HONO upon irradiation compared to solutions containing only one of the molecular proxies. This suggests that synergistic effects in the HONO formation can occur in complex organic samples. Atmospheric implications of the results presented here are discussed.

Children at the beginning of their formal educational experiences are developing cognitively, socially, and emotionally. However, due to the ramifications of the COVID-19 pandemic, many children are behind in these developmental areas. Self-awareness is a foundation of students' understandings of themselves and the world around them. Without a greater perception of self-awareness, children learning to read in the primary grades might struggle in self-efficacy and motivation, and a lack of self-awareness can even affect students' understandings of texts in later grades. This article focuses on how teachers can use children's literature to discuss socioemotional components and to help students recognize who they are as learners and readers.

Phenol, a common semi-volatile compound associated with different emissions including from plants and biomass burning, as well as anthropogenic emissions and its derivatives, are important components of secondary organic aerosols (SOAs). Gas and aqueous phase reactions of phenol, in the presence of photochemical drivers, are fairly well understood. However, despite observations showing aromatic content within SOA size and mass increases during dust episodes, the heterogeneous reactions of phenol with mineral dusts are poorly understood. In the current study, surface reactions of phenol at the gas/solid interface with different components of mineral dust including SiO2, α-Fe2O3, and TiO2 have been investigated. Whereas reversible surface adsorption of phenol occurs on SiO2 surfaces, for both α-Fe2O3 and TiO2 surfaces, phenol reacts to form a wide range of OH substituted aromatic products. For α-Fe2O3 surfaces that have been nitrated by gas-phase reactions of nitric acid prior to exposure to phenol, unique compounds form on the surface including nitro-phenolic compounds. Moreover, additional surface chemistry was observed when adsorbed nitro-phenolic products were exposed to gas-phase SO2 as a result of the formation of adsorbed nitrite from nitrate redox chemistry with adsorbed SO2. Overall, this study reveals the extensive chemistry as well as the complexity of reactions of prevalent organic compounds leading to the formation of SOA on mineral surfaces.

BACKGROUND: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. METHODS AND FINDINGS: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochrans Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. CONCLUSIONS: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

Fluorescent proteins (FPs) are ubiquitous tools in research, yet their endogenous functions in nature are poorly understood. In this work, we describe a combination of functions for FPs in a clade of intertidal sea anemones whose FPs control a genetic color polymorphism together with the ability to combat oxidative stress. Focusing on the underlying genetics of a fluorescent green "Neon" color morph, we show that allelic differences in a single FP gene generate its strong and vibrant color, by increasing both molecular brightness and FP gene expression level. Natural variation in FP sequences also produces differences in antioxidant capacity. We demonstrate that these FPs are strong antioxidants that can protect live cells against oxidative stress. Finally, based on structural modeling of the responsible amino acids, we propose a model for FP antioxidant function that is driven by molecular surface charge. Together, our findings shed light on the multifaceted functions that can co-occur within a single FP and provide a framework for studying the evolution of fluorescence as it balances spectral and physiological functions in nature.

Autism disclosure can be a complicated decision that autistic people experience. Positive outcomes can include feelings of acceptance and support, but negative outcomes can include stigma and discrimination. Although a surge in research on this topic has led to more understanding around autism disclosure, the methodologies used may have limited who was contributing to the conversation and data. To overcome this, we analyzed 3 years (2020-2022) of social media data (Reddit and Twitter) as this was public information that did not rely on researcher data collection. Reflexive thematic analysis of 3121 posts led to the generation of four themes: People do not understand autism (with experiences related to employment, dating, healthcare and mental health), autistic people just want privacy and respect, autistic people can lead us forward and non-autistic people need to assume more responsibility. We discuss how autistic adults experience the impact of society's lack of understanding of autism on a daily basis whether they disclose or not, and that it is everybody's responsibility to challenge negative stereotypes and promote a more inclusive society.

Autistic people's perceptions of their interactions with criminal justice professionals are predominantly negative; however, little is known about the state of interactions on a global scale. To further understanding, a comprehensive stakeholder questionnaire was created. Aspects of reliability and validity including evidence for test content and internal structure were gathered using expert reviews, cognitive interviewing, pilot data collection, and a larger data collection effort (N = 1618). Data was gathered from the autism community through perspectives of parents/caregivers as well as from self-reported autistic adults. Criminal justice professionals included law enforcement officers, corrections professionals, probation and parole officers, forensic psychologists and legal professionals. The scale development process was detailed in order to sufficiently document the initial psychometric evidence and share the steps taken to gain diverse stakeholder input. This study is a critical first step in generating further information to facilitate policy and program development with wide applicability.

BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES: This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION: Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end pointagnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001, Introduction Chemical hazard and risk assessments aim to estimate toxicological outcomes and evaluate the potential risks that chemicals may pose to human health and the environment. However, studying chemicals one-by-one [...]

Employing an institutional case study approach, this manuscript features two high-impact learning opportunities open to Albion College students who seek to contribute to solving real-world problems in collaboration with local and surrounding communities: Albion College Community Collaborative (AC3) and Human Rights Lab. The programmatic approach to community-engaged partnerships featured sought to elevate local knowledge, promote engaged citizenship, and advance social, political, and economic change. We describe each program followed by lessons learned and critical considerations to advance the field and to inform the work of others seeking to employ a programmatic approach.