Expanding Kane's argument-based validity framework: What can validation practices in language assessment offer health professions education?

CONTEXT: One central consideration in health professions education (HPE) is to ensure we are making sound and justifiable decisions based on the assessment instruments we use on health professionals. To achieve this goal, HPE assessment researchers have drawn on Kane's argument-based framework to ascertain the validity of their assessment tools. However, the original four-inference model proposed by Kane - frequently used in HPE validation research - has its limitations in terms of what each inference entails and what claims and sources of backing are housed in each inference. The under-specification in the four-inference model has led to inconsistent practices in HPE validation research, posing challenges for (i) researchers who want to evaluate the validity of different HPE assessment tools and/or (ii) researchers who are new to test validation and need to establish a coherent understanding of argument-based validation. METHODS: To address these identified concerns, this article introduces the expanded seven-inference argument-based validation framework that is established practice in the field of language testing and assessment (LTA). We explicate (i) why LTA researchers experienced the need to further specify the original four Kanean inferences; (ii) how LTA validation research defines each of their seven inferences and (iii) what claims, assumptions and sources of backing are associated with each inference. Sampling six representative validation studies in HPE, we demonstrate why an expanded model and a shared disciplinary validation framework can facilitate the examination of the validity evidence in diverse HPE validation contexts. CONCLUSIONS: We invite HPE validation researchers to experiment with the seven-inference argument-based framework from LTA to evaluate its usefulness to HPE. We also call for greater interdisciplinary dialogue between HPE and LTA since both disciplines share many fundamental concerns about language use, communication skills, assessment practices and validity in assessment instruments.

Interventions to decrease health students' stigma toward schizophrenia: A scoping review.

BACKGROUND: Schizophrenia is heavily stigmatized among health professionals. Given that health professional students are future members of the workforce and will provide care for people with schizophrenia, it is essential to implement interventions aimed at reducing stigma among this group. OBJECTIVE: This scoping review aimed to identify and synthesize existing literature on interventions to decrease schizophrenia stigma among health professional students, and to determine the possible gaps in the literature. DESIGN: Nine electronic databases and gray literature were searched, including PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature, PsycINFO, MEDLINE, Web of Science, Scopus, China National Knowledge Infrastructure, WanFang, and Google on 5 May 2023. Two researchers independently conducted data screening, data extraction, and assessed study risks. A most updated search was also done on 22 May 2024. The Cochrane risk of bias tool version 2 for randomized trials and Risk of Bias in Non-randomized Studies were used to assess the studies' risk of bias. Data synthesis and analysis were conducted by two reviewers using a narrative approach. Reporting adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. RESULTS: This review included twenty-one studies with 2520 health professional students. The majority of included studies were non-randomized controlled trials (38 %) and pre-post studies (52 %). Most of the included studies were conducted in the United States (24 %). The participants in ten (48 %) studies were medical students. The number of intervention sessions ranged from one to 13, with an average of three. Seven (33 %) studies had an intervention duration of less than four weeks and 16 (76 %) studies had no follow-up. Various scales were used to assess the outcomes of schizophrenia stigma. Only two studies (10 %) indicated the intervention's ineffectiveness, with the majority of interventions led by psychiatry department faculty and individuals with schizophrenia. CONCLUSIONS: Most studies (90 %) utilized various approaches, including face-to-face or online education, direct contact with individuals with schizophrenia, or a combination thereof, to diminish stigma among health professional students. However, none addressed cultural and empathy factors in their intervention designs, and the included studies lacked theoretical guidance. The review only comprised English quantitative studies with significant heterogeneity, with 17 studies (81 %) displaying serious or high risk of bias, limiting comprehensive discussions. These findings offer valuable insights for future systematic review. Tweetable abstract Studies on reducing health professional students' schizophrenia stigma need to address cultural and empathy factors.

Geographic clustering of cannabis stores in Canadian cities: A spatial analysis of the legal cannabis market 4 years post-legalisation.

INTRODUCTION: Following the legalisation of non-medical cannabis in 2018, the number of cannabis stores in Canada has rapidly expanded with limited regulation on their geographic placement. This study characterised the clustering of cannabis stores in Canadian cities and evaluated the association of clustering with provincial policy and sociodemographic variables. METHODS: Cross-sectional spatial analysis of cannabis store density in dissemination areas ('neighbourhoods', n = 39,226) in Canadian cities in September 2022. Cannabis store density was defined as the count of stores within 1000 m of a neighbourhood centre. Clusters of high-density cannabis retail were identified using Local Indicators of Spatial Autocorrelation. Associations between provincial policy (privatised vs. public market), sociodemographic variables and cannabis store density were evaluated using multivariable regression. RESULTS: Clusters of high-density cannabis retail were identified in 86% of Canadian cities, and neighbourhoods in clusters had a median of 5 stores within 1000 m. Toronto, Canada's most populous city, had the most extreme clustering where neighbourhoods in clusters had a median of 10 stores (and a maximum of 25 stores) within 1000 m. Neighbourhoods in private versus public retail markets had a significantly higher neighbourhood-level density of cannabis stores (adjusted rate ratio [aRR] 63.37, 95% confidence interval [CI] 25.66-156.33). Lower neighbourhood income quintile was also associated with a higher neighbourhood-level density of cannabis stores (Q5 vs. Q1, aRR 1.28, 95% CI 1.17-1.40). DISCUSSION AND CONCLUSIONS: Since cannabis was legalised, clusters of high-density cannabis retail have emerged in most Canadian cities and were more likely to form lower income neighbourhoods and in private retail markets.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Identifying Mechanisms and Therapeutic Targets in Muscle using Bayesian Parameter Estimation with Conditional Variational Autoencoders.

Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially preventing such damage. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with both control and the cardiac Troponin C (cTnC) I61Q variant in mouse trabeculae and model parameters that recover the abnormal I61Q cTnC twitches. SIGNIFICANCE: Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.

Evolution of the Surgical Net.

The surgical net technique, originally conceptualized to mitigate postoperative hematomas, has evolved into a versatile tool, transcending its initial purpose, and signaling a new era of surgical innovation. This article provides a comprehensive overview of the surgical net's journey from a targeted solution to a multifaceted surgical asset and explores its burgeoning applications, challenges, and future trajectories. Despite early reluctance due to anatomical concerns and unfamiliarity with the novel technique, the surgical net's consistent success in various surgical contexts from facial surgeries to gynecological applications has catalyzed its widespread global adoption. The technique's integration of the progressive tension method and enhanced skin redraping capabilities underscores its contribution to optimal aesthetic outcomes and improved surgical control. Noteworthy is its role in the innovative concept of gliding surgery, marked by procedures like the gliding brow lift and Glidelift, demonstrating the surgical net's adaptability and effectiveness in diverse surgical environments.Contributions from international surgeons have expanded its applications to areas like axillary space obliteration after breast implant, postmastectomy closure, and the management of postpartum hemorrhage, showcasing the surgical net's global impact and universal commitment to optimizing surgical outcomes. The article delves into the anatomical considerations essential for adapting the surgical net technique to various surgical contexts, emphasizing the need for continuous research, clinical trials, and multidisciplinary collaboration to navigate emerging challenges and ensure its sustained efficacy and safety.In conclusion, the article highlights the surgical net technique's unwavering potential for further advancements, greater procedural efficiency, and the continued elevation of patient care standards. The consistent evolution of its applications, paired with a dedicated approach to addressing emergent challenges, reinforces the surgical net's enduring contribution to surgical innovation and global patient welfare.

Do WHO criteria for gestational diabetes fit a rural population in Tanzania? - A follow-up study assessing mother and child health six years after a pregnancy diagnosed with gestational diabetes.

AIMS AND METHODS: In low- and middle- income countries (LMICs) consequences of gestational diabetes (GDM) is understudied. Using a prospective cohort of mothers (n = 197)and children (n = 251), from rural north-eastern Tanzania, we assessed prediabetes and type 2 diabetes (T2D) prevalence six years after a pregnancy with/without GDM. RESULTS: The prevalence of prediabetes (49.4 % vs. 46.4 %) orT2D (20.0 % vs. 16.1 %), p ≥ 0.36, based on fasting plasma glucose (FPG) or HbA1clevels (prediabetes: 16.9 % vs. 13.8 % and T2D 1.2 % vs. 0 %, p = 0.47), andcardio-metabolic health parameters,weresimilar between women with/without previous GDM. These results were supported by similar perinatal outcomes and child health at follow-up.The overall prevalence ofprediabetes/T2D was high, but no differences in other cardio-metabolic risk markers were observed in women with prediabetes/T2D compared to women with normal glucose tolerance. CONCLUSIONS: Despite high prevalence of GDM amongTanzanian women, the diagnosis was not associated with adverse pregnancy outcomes, nor with increased risk of prediabetes or T2D at follow-up. FPG and HbA1c may be poor markers for diabetes in this population, and further follow-up studies with longer time intervals are warranted to evaluate which GDM diagnostic criteria are most optimal for women in rural Tanzania and similar LMIC settings.

Imaging features and surgical management of giant parathyroid adenoma with autoinfarction.

Autoinfarction of a parathyroid adenoma can have an atypical clinicoradiologic features that can mimic an inflammatory process or malignancy. In addition, the associated fibrosis makes surgical resection more challenging than for regular parathyroid adenomas. The implications of these findings are that while autoinfarction of parathyroid adenomas is a rare phenomenon, this entity should be considered when there are heterogeneous and cystic components on imaging in patients without hypercalcemia. Ultimately, histopathology is necessary for definitive diagnosis.

Self-Perception of Extroversion Increases with Social Media Use: A Social Media Induction Task.

Previous studies have examined the relationship between social media and extroversion, often relying on correlational designs. This study introduced a social media induction procedure to examine the relationship between social media and extroversion with an experimental design. In a procedure adapted from the personality research literature, participants used a written prompt to imagine themselves as a social media influencer and were then asked to use social media with this mindset. Participants' extroversion was measured before and after this activity using the Big Five Inventory. The results show that this social media induction procedure resulted in increased reports of extroversion after the task, and this post-task change was not predicted by other related social media factors (such as hours of social media use per day). These results complicate the assertion that differences in social media use between extroverts and introverts are due to its stronger appeal to extroverts.

Determining the distance patterns in the movements of future doctors in UK between 2002 and 2015: a retrospective cohort study.

OBJECTIVE: To determine and identify distance patterns in the movements of medical students and junior doctors between their training locations. DESIGN: A retrospective cohort study of UK medical students from 2002 to 2015 (UKMED data). SETTING: All UK medical schools, foundations and specialty training organisation. PARTICIPANTS: All UK medical students from 2002 to 2015, for a total of 97 932 participants. OUTCOME MEASURES: Individual movements and number of movements by county of students from family home to medical school training, from medical school to foundation training and from foundation to specialty training. METHODS: Leslie matrix, principal components analysis, Gini coefficient, χ2 test, generalised linear models and variable selection methods were employed to explore the different facets of students' and junior doctors' movements from the family home to medical school and for the full pathway (from family home to specialty training). RESULTS: The majority of the movements between the different stages of the full pathway were restricted to a distance of up to 50 km; although the proportion of movements changed from year-to-year, with longer movements during 2007-2008. At the individual level, ethnicity, socioeconomic class of the parent(s) and the deprivation score of the family home region were found to be the most important factors associated with the length of the movements from the family home to medical school. Similar results were found when movements were aggregated at the county level, with the addition of factors such as gender and qualification at entry (to medical school) being statistically associated with the number of new entrant students moving between counties. CONCLUSION: Our findings show that while future doctors do not move far from their family home or training location, this pattern is not homogeneous over time. Distances are influenced by demographics, socioeconomic status and deprivation. These results may contribute in designing interventions aimed at solving the chronic problems of maldistribution and underdoctoring in the UK.

Anaemia in the first trimester and poor physiological plasma expansion during pregnancy negatively impact foetal weight and newborn anthropometrics: An observational cohort study in Tanzania.

OBJECTIVES: Anaemia during pregnancy is a major health challenge affecting pregnancy outcome worldwide. The objectives of this study were to investigate the impact of severe-moderate anaemia in the first trimester, as well as changes in haemoglobin during pregnancy among non-anaemic women, on foetal weight, placental blood flow and newborn anthropometrics. METHODS: In a prospective cohort study, 346 women residing in rural Tanzania were followed throughout pregnancy with serial ultrasound and newborn anthropometrics assessed within 24 h of delivery. Associations between placental blood flow, foetal weight and newborn anthropometrics with either first trimester severe-moderate anaemia (haemoglobin≤9.5 g/dL) or changes in haemoglobin from the first to the third trimester among non-anaemic women, were assessed by mixed model regression and multiple linear regression, adjusting for maternal and foetal co-variables. Foetal weights and birthweight were converted to z-scores using a population based sex-specific weight reference. RESULTS: Severe-moderate anaemia in the first trimester was associated with significantly reduced foetal weight z-scores (adjusted mean difference (aMD) -0.44 (95% CI -0.81, -0.07)) and newborn anthropometric indices (birth weight z-score aMD -0.55 (-0.9, -0.13), abdominal circumference aMD -11 mm (95% CI -20, -3)). There were no association between first trimester severe-moderate anaemia and placental blood flow. Among women who were non-anaemic in the first trimester, women with the least reduction in haemoglobin (Δ ≥ -0.3 g/dL) delivered significantly smaller newborns (birthweight z-score aMD -0.55 (-0.91, -0.20), abdominal circumference aMD -10 mm (95% CI -17, -3), compared to women with the greatest reduction (Δ haemoglobin ≤ -1.4 g/dL)). CONCLUSIONS: Severe-moderate anaemia in early pregnancy was associated with smaller newborn anthropometrics which was reflected in smaller mean foetal weights in the second and third trimester. Furthermore, among women who were non-anaemic in the first trimester, there was an association between smaller newborn anthropometrics and limited haemoglobin decrease during pregnancy, possibly reflecting insufficient plasma expansion.

Impact of non-medical cannabis legalization with market restrictions on health service use and incident cases of psychotic disorder in Ontario, Canada.

BACKGROUND: Cannabis is a risk factor in the onset and persistence of psychotic disorders. There is concern that non-medical cannabis legalization in Canada may have population-level impacts on psychotic disorders. We sought to examine changes in health service use and incident cases of psychotic disorder following cannabis legalization, during a period of tight restrictions on retail stores and product types. METHODS: We conducted a cross-sectional interrupted time-series analysis using linked population-based health administrative data from Ontario (Canada) from January 2014 to March 2020. We identified psychosis-related outpatient visits, emergency department visits, hospitalizations, and inpatient length of stay, as well as incident cases of psychotic disorders, among people aged 14 to 60 years. RESULTS: We did not find evidence of increases in health service use or incident cases of psychotic disorders over the short-term (17 month) period following cannabis legalization. However, we found clear increasing trends in health service use and incident cases of substance-induced psychotic disorders over the entire observation window (2014-2020). CONCLUSION: Our findings suggest that the initial period of tight market restriction following legalization of non-medical cannabis was not associated with an increase in health service use or frequency of psychotic disorders. A longer post-legalization observation period, which includes expansion of the commercial cannabis market, is needed to fully understand the population-level impacts of non-medical cannabis legalization; thus, it would be premature to conclude that the legalization of non-medical cannabis did not lead to increases in health service use and incident cases of psychotic disorder.

The effect of non-medical cannabis retailer proximity on use of mental health services for psychotic disorders in Ontario, Canada.

BACKGROUND: Cannabis is associated with the onset and persistence of psychotic disorders. Evidence suggests that accessibility of substances is associated with an increased risk of use-related harms. We sought to examine the effect of residing in proximity to non-medical cannabis retailers on the prevalence of health service use for psychosis. METHODS: We conducted a cross-sectional study using linked health administrative data, and used geospatial analyses to determine whether people in Ontario, Canada (aged 14-60 years) resided within walking (1.6 km) or driving (5.0 km) distance of non-medical cannabis retailers (open as of February-2020). We identified outpatient visits, emergency department (ED) visits, and hospitalizations for psychotic disorders between 01-April-2019 and 17-March-2020. We used zero-inflated Poisson regression models and gamma generalized linear models to estimate the association between cannabis retailer proximity and indicators of health service use. RESULTS: Non-medical cannabis retailers were differentially located in areas with high levels of marginalization and pre-existing health service use for psychosis. People residing within walking or driving distance of a cannabis retailer had a higher rate of psychosis-related outpatient visits, ED visits, and hospitalizations, compared to people living outside these areas. This effect was stronger among those with no prior service use for psychosis. CONCLUSIONS: Proximity to a non-medical cannabis retailer was associated with higher health service use for psychosis, even after adjustment for prior health service use. These findings suggest that opening of non-medical cannabis retailers could worsen the burden of psychosis on mental health services in areas with high-risk populations.

Mortality in adolescents and young adults following a first presentation to the emergency department for alcohol.

BACKGROUND AND METHODS: We conducted a population-based, retrospective cohort study of first-time emergency department (ED) visits in adolescents and young adults (AYA) due to alcohol and compared mortality to AYA with nonalcohol ED visits between 2009 and 2015 using standardized all-cause mortality ratios (age, sex, income, and rurality). We described the cause of death for AYA and examined the association between clinical factors and mortality rates in the alcohol cohort using proportional hazard models. RESULTS: A total of 71,776 AYA had a first-time ED visit due to alcohol (56.1% male, mean age 20.7 years) between 2009 and 2015, representing 3.3% of the 2,166,838 AYA with an ED visit in this time period. At 1 year, there were 2396 deaths, 248 (10.3%) following an ED visit related to alcohol. First-time alcohol ED visits were associated with a threefold higher risk in mortality at 1 year (0.35% vs. 0.10%, adjusted hazard ratio [aHR] 3.07, 95% confidence interval [CI] 2.69-3.51). Mortality was associated with age 25-29 years (aHR 3.88, 95% CI 2.56-5.86), being male (aHR 1.98, 95% CI 1.49-2.62), having a history of mental health or substance use (aHR 3.22, 95% CI 1.64-6.32), cause of visit being withdrawal/dependence (aHR 2.81, 95% CI 1.96-4.02), and having recurrent ED visits (aHR 1.97, 95% CI 1.27-3.05). Trauma (42.7%), followed by poisonings from drugs other than opioids (38.3%), and alcohol (28.6%) were the most common contributing causes of death. CONCLUSION: Incident ED visits due to alcohol in AYA are associated with a high risk of 1-year mortality, especially in young adults, those with concurrent mental health or substance use disorders, and those with a more severe initial presentation. These findings may help inform the need and urgency for follow-up care in this population.

Automated AUP (absorption under pressure): A novel method to characterise superabsorbent polymers.

Superabsorbent polymers (SAPs) are a key constituent in baby diapers and adult incontinence products. To develop new SAPs, lab tests are used to measure properties that are considered important for these products. However, these manual tests are often time-consuming and tedious. This paper introduces an automatic method to measure absorption under pressure (aAUP) using industry robots. The new robotic method addresses limitations in an industry standard absorption under pressure (AUP) method. The new aAUP method can run continuously, generating more and better data to aid in product development. It enables to put the Buchholz theory on SAPs for predicting their properties to practice by reduction of manual effort and by producing excellent data quality. This has been shown on commercial polymers from different production processes, with varied particle size distributions and shapes. The aAUP data has also been shown to accurately predict other properties of SAPs relevant for their application in hygiene articles, making it a useful tool for understanding the underlying science and facilitating product development.

Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta.

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

Effect of a curriculum transformation on pharmacy student self-efficacy, self-reported activities, and satisfaction in degree and career choice.

BACKGROUND: Curriculum revision in healthcare programs occurs frequently, but to undergo a whole degree transformation is less common. Also, the outcomes of curriculum redesign interventions on the selfreported clinical decision making, experiences, and perceptions of graduates of health education programs is unclear. This study evaluated these factors as an outcome of a pharmacy degree whole-curriculum transformation. METHODS: A 25-item cross-sectional end-of-course survey was developed to evaluate pharmacy student decisions, experiences, and perceptions upon completion of degree, pre- and post- curriculum transformation. A two-way analysis of variance (ANOVA) was used to determine whether the responses to the items classed within the main factors differed across the two cohorts. Independent t-tests were used to examine the student responses to the individual questions between the two cohorts. RESULTS: Graduates from the transformed degree had greater self-efficacy in clinical activities, were more satisfied with their education, found course activities more useful, and were more confident in their career choice. Transformed pharmacy degree students also reported spending more time on weekdays and weekends on activities such as attending lectures and working. Student satisfaction with their choice to attend pharmacy school was also significantly higher in transformed degree students. CONCLUSIONS: Responses to the end of degree survey indicate that students who completed the transformed pharmacy curriculum have had positive experiences throughout their degree and felt more prepared for practice as pharmacists in comparison to students who completed the established degree. These results add value to those collected from other sources (e.g., student evaluations, assessment scores, preceptors focus groups, and other stakeholder inputs) consistent with a comprehensive quality improvement model.

Acute care related to cannabis use during pregnancy after the legalization of nonmedical cannabis in Ontario.

BACKGROUND: Cannabis use during pregnancy is increasing, but the contribution of cannabis legalization to these trends is unclear. We sought to determine whether health service utilization related to cannabis use during pregnancy increased after the legalization of nonmedical cannabis in October 2018 in Ontario, Canada. METHODS: In this population-based, repeated cross-sectional study, we evaluated changes in the number of pregnant people who received acute care (emergency department visit or admission to hospital) between January 2015 and July 2021 among all people eligible for the province's public health coverage. We used segmented regression to compare changes in the quarterly rate of pregnant people with acute care related to cannabis use (primary outcome) with the quarterly rates of acute care for mental health conditions or for noncannabis substance use (control conditions). We identified risk factors associated with acute care for cannabis use and the risk of adverse neonatal outcomes using multivariable logistic regression models. RESULTS: The mean quarterly rate of acute care for cannabis use during pregnancy increased from 11.0 per 100 000 pregnancies before legalization to 20.0 per 100 000 pregnancies after legalization (incidence rate ratio [IRR] 1.82, 95% confidence interval [CI] 1.44-2.31), while acute care for mental health conditions decreased (IRR 0.86, 95% CI 0.78-0.95) and acute care for noncannabis substance use did not change (IRR 1.03, 95% CI 0.91-1.17). Legalization was not associated with an immediate change, but the quarterly change in rates of pregnancies with acute care for cannabis use increased by 1.13 (95% CI 0.46-1.79) per 100 000 pregnancies after legalization. Pregnant people with acute care for cannabis use had greater odds of having received acute care for hyperemesis gravidarum during their pregnancy than those without acute care for cannabis use (30.9% v. 2.5%, adjusted odds ratio [OR] 9.73, 95% CI 8.01-11.82). Pregnancies with acute care for cannabis use had greater odds of newborns being born preterm (16.9% v. 7.2%, adjusted OR 1.93, 95% CI 1.45-2.56) and of requiring care in the neonatal intensive care unit (31.5% v. 13.0%, adjusted OR 1.94 95% CI 1.54-2.44) than those without acute care for cannabis use. INTERPRETATION: The rate of acute care related to cannabis use during pregnancy almost doubled after legalization of nonmedical cannabis, although absolute increases were small. These findings highlight the need to consider interventions to reduce cannabis use during pregnancy in jurisdictions pursuing legalization.

Nonuniform structural properties of wings confer sensing advantages.

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations during each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients and are subject to both aerodynamic and structural damping. Here we examine how both of these properties impact sensory performance, using finite element analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

Machine learning meets Monte Carlo methods for models of muscle's molecular machinery to classify mutations.

The timing and magnitude of force generation by a muscle depend on complex interactions in a compliant, contractile filament lattice. Perturbations in these interactions can result in cardiac muscle diseases. In this study, we address the fundamental challenge of connecting the temporal features of cardiac twitches to underlying rate constants and their perturbations associated with genetic cardiomyopathies. Current state-of-the-art metrics for characterizing the mechanical consequence of cardiac muscle disease do not utilize information embedded in the complete time course of twitch force. We pair dimension reduction techniques and machine learning methods to classify underlying perturbations that shape the timing of twitch force. To do this, we created a large twitch dataset using a spatially explicit Monte Carlo model of muscle contraction. Uniquely, we modified the rate constants of this model in line with mouse models of cardiac muscle disease and varied mutation penetrance. Ultimately, the results of this study show that machine learning models combined with biologically informed dimension reduction techniques can yield excellent classification accuracy of underlying muscle perturbations.