The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics. Even since Watson and Crick  first inhttp://omicsmaps.com/ (accessed on 3 September 2023)) on nearly every continent [https://portal.gdc.cancer.gov/ (accessed on 15 September 2023)), dbGaP (https://www.ncbi.nlm.nih.gov/gap (accessed on 15 September 2023)), and GEO (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 15 September 2023)), just to name a few. A combination of various data can bring about deeper insights into genes so as to help researchers locate the information of interest.Genomic research aims to understand the genomes of different species. It studies the roles assumed by multiple genetic factors and the way they interact with the surrounding environment under different conditions. In contrast to genetics, which deals with a limited number of specific genes, genomics takes a global view that involves the entirety of genes possessed by an organism . For exaontinent . The vasontinent . Most ofOn the other hand, the development of deep learning methods has granted the computational power to resolve these complex research questions ,11. Its The intersection of deep learning methods and genomic research may lead to a profound understanding of genomics that will benefit multiple fields including precision medicine , pharmacThe rest of this article is organized as follows: we first briefly introduce the genomic study powered by deep learning characterized by deep learning architectures in Various deep learning algorithms have their own advantages to resolve particular types of problems in genomic applications  are one of the most successful deep learning models for image processing owing to their outstanding capacity to analyze spatial information. Early applications of CNNs in genomics relied on the fundamental building blocks of CNNs in computer vision  to extraThe highlight of CNNs is the dexterity of automatically performing adaptive feature extraction during the training process. For instance, CNNs can be applied to discover meaningful recurring patterns with small variances, such as genomic sequence motifs. This makes CNNs suitable for motif identification and therefore binding classification .Recently, CNNs have been shown to take a lead among current algorithms for solving several sequence-based problems. Alipanahi et al. , DeepBinhttps://github.com/Mri-monitoring/Mri-docs/blob/master/mriapp.rst (accessed on 14 September 2023)) and mainly examined the performance of nine variants of CNNs, and they concluded that CNNs do not need to be deep for motif discovery tasks as long as the structure is appropriately designed. When applying CNNs in genomics, simply changing the network depth would not account for much improvement in model performance. This is because deep learning models are usually over-parameterized, meaning there are more parameters in the neural network than what is actually required to complete the task [Although multiple researchers have demonstrated the superiority of CNNs over other existing methods, inappropriate structure design would still result in even poorer performance than conventional models. For example, Zeng et al.  conductethe task . In thisthe task  designedthe task ,45 took Recurrent neural networks (RNNs) raised a surge of interest owning to their impressive performance on sequential prediction problems such as language translation, summarization, and speech recognition. RNNs outperform CNNs and other early deep neural networks (DNNs) on sequential data thanks to their capability of processing long ordered sequences and memorizing long-range information through recurrent loops. Specifically, RNNs scan the input sequences sequentially and feed both the previously hidden layer and current input segment as the model input so that the final output implicitly integrates both current and previous information in the sequence. Schuster and Paliwal  later prThe cyclic structure makes a seemingly shallow RNN over long-time prediction actually very deep if unrolled in time. To resolve the vanishing gradient problem rendered by this, Hochreiter and Schmidhuber  substituGenomics data are typically sequential and often considered languages of biological nature. Recurrent models are thus applicable in many scenarios. For example, Cao et al.  , denoisVariational autoencoders (VAEs), though named “autoencoders”, were rather developed as an approximate-inference method to model latent variables. Based on the structure of autoencoders, Kingma and Welling  added stAs deep learning is constantly showing success in genomics, researchers are expecting deep learning to show higher accuracy than simply outperforming statistical or machine learning methods. To this end, the vast majority of work nowadays approaches genomic problems from more advanced architectures beyond classic deep architectures or employing hybrid models. Here, we review some examples of recent appearing deep architectures which skillfully modify or combine classical deep learning models.Most of these emergent advanced architectures are of natural designs modified from classic deep learning models. Researchers began to leverage more genomic intuitions to fit each particular problem with a more advanced and suitable model.Motivated by the fact that protein folding is a progressive refinement  rather tThe fact that each type of DNN has its own strength inspires researchers to develop hybrid architectures that could well utilize the potential of multiple deep learning architectures. DanQ  is a hybHybrid architectures allow flexible network design by selecting specific components with proven success representing different types of information in genomic sequences. For example, in both ,62, CNN As mentioned in Transformer-based  languageInstead of processing input tokens one by one sequentially as RNNs do, transformers process all input tokens more efficiently at the same time in parallel. However, simply increasing the input context window infinitely is infeasible, since the computation time and memory scale quadratically with context length in the attention layers. Several improvements have been made from different perspectives: Nguyen et al.  uses theIn light of dealing with extremely long-range interactions in DNA sequences, the Enformer model  employs Even with all these advancements in efficiency improvement, the significant training and serving cost still remains a challenging problem for LLMs , especiaApplications of deep learning in genomic problems have fully proven their power. Although the pragmatism of deep learning is surprisingly successful, this method suffers from lacking the physical transparency to be well interpreted so as to better assist the understanding of genomic problems. What is auspicious in genomic research is that researchers have conducted lots of work to visualize and interpret their deep learning models. It is also constructive to take into additional considerations beyond the choice of deep learning architectures. In this section, we review some visualization techniques that bring about insights into deep learning architectures and add remarks on model design that might be conducive to real-world applications.People expect deep networks to succeed not only in predicting results but also in identifying meaningful DNA sequence signals and giving further insights into the problems being solved. The interpretability of a model appears to be crucial when it comes to application. However, the technology of deep learning has exploded not only in prediction accuracy but also in complexity as well. Connections among network units are so convoluted that the information is widespread throughout the network and thus perplexing to be captured . People The image classification field is where people started deciphering deep networks. Zeiler and Fergus  gave insThe rise of attention-based models also opened up new avenues for interpretability in genomics. Singh et al.  argued tIt needs to be addressed that there still is no universal framework for an interpretability study of deep neural networks in the genomics domain. For example, although many models referred to in this paper ,68,70,71The concept of transfer learning is naturally motivated by human intelligence whereby people can apply the knowledge that has already been acquired to address newly encountered problems. Transfer learning is such a framework that allows deep learning to adapt the previously trained model to exploit a new but relevant problem more effectively . It has http://www.image-net.org/ (accessed on 3 September 2023)) to extract general features at different scales and then fine-tuned the model by multitask learning to capture CV term-specific discriminative information. Liu et al. [Early adaptation of transfer learning in genomics was based on machine learning methods such as SVMs ,131,132.u et al.  developeu et al.  transferu et al.  , just to name a few. The previously mentioned ultra-DNN  is a casIn this section, we review several aspects of genomic problems that can be approached from deep learning methods and discuss how deep learning moves forward in these fields. A summary of the taxonomy of different deep learning application areas with corresponding deep learning models discussed in this section can be found in Gene expression is a highly regulated process by which the genetic instructions in DNA are converted into functional products such as proteins and other molecules, and they also respond to the changing environment accordingly. Namely, genes encode protein synthesis and self-regulate the functions of the cell by adjusting the amount and type of proteins it produces . Here, wAn increasing number of genome-wide gene expression assays for different species have become available in public databases; e.g., the Connectivity Map (CMap) project was launched to create a reference collection of gene expression profiles that can be used to identify functionally connected molecules . These dThe empirical results of early works that applied principal component analysis (PCA) on gene expression data to capture cluster structure have shown that this mathematical tool was not effective enough to allow some complicated biological considerations . Also, sDenoising autoencoders came in handy since they do not merely retain the information of raw data but also generalize meaningful and important properties of the input distribution across all input samples. Even shallow denoising autoencoders can be proven effective in extracting biological insights. Danaee et al.  adopted Some other works moved to variational inference in autoencoders, which is assumed to be more skillful at capturing the internal dependencies among data. Way and Greene  trained Another thread for utilizing deep learning to characterize gene expression is to describe the pairwise relationship. Wang et al.  showed tDeep learning approaches for gene expression prediction have outperformed other existing algorithms. For example, Chen et al.  presenteWhen making predictions from gene sequences, deep learning models have been shown fruitful in identifying the context-specific roles of local DNA-sequence elements; then, the further inferred regulatory rules can be used to predict expression patterns . SuccessIn most applications, the power of deep learning algorithms is impeded by biological restrictions. Therefore, instead of only using sequence information, combing epigenetic data into the model might add to the explanatory power of the model. For example, the correlation between histone modifications and gene regulation was suggested experimentally in Lim et al. , Cain etGenerative models were also adopted due to the ability to capture high-order, latent correlations. For example, to explore hypothetical gene expression profiles under various types of molecular and genetic perturbation, Way and Greene  trained Gene expression regulation is the cellular process that controls the expression level of gene products (RNA or protein) to be high or low. It increases the versatility of an organism so as to allow it to react and adapt to the surrounding environment. The underlying interdependencies behind the sequences limit the flexibility of conventional methods, but deep networks that could model an over-representation of sequence information have the potential to allow regulatory motifs to be identified according to their target sequences.The most efficient way of gene expression regulation for an organism is at the transcriptional level, which occurs at the early stage of gene regulation. Enhancers and promoters are two of the most well-characterized types of functional elements in the regions of noncoding DNA, which belong to cis-regulatory elements (CREs). Readers can visit Wasserman and Sandelin  and Li ePromoters locate near the transcription start sites of genes and thereby initiate the transcription of particular genes. Conventional algorithms still perform poorly on promoter prediction, while the prediction is always accompanied by a high false positive rate . The comPEDLA was developed by Liu et al.  as an alMin et al.   is an attempt taken by Li et al.  to introEnhancer–promoter interaction predictions are always based on non-sequence features from functional genomic signals. Singh et al.  (SPEID) LLMs has made significant progress in promoter and enhancer-related prediction tasks. Ji et al.  utilizedSplicing refers to the editing of pre-messenger RNA so as to produce a mature messenger RNA (mRNA) that can be translated into a protein. This process effectively adds up to the diversity of protein isoforms. Predicting a “splicing code” aims to understand how splicing regulates and manifests the functional changes of proteins, and it is crucial for understanding different ways of how proteins are produced.Initial machine learning attempts included the naive Bayes model  and two-In many cases, the phenomenon of alternative splicing occurs. That is, a single gene might end up coding for multiple unique proteins by varying the exon composition of the same mRNA during the splicing process. This is a key post-transcriptional regulatory mechanism that affects gene expression and contributes to proteomic diversity . Leung eTranscription factors (TFs) refer to proteins that bind to promoters and enhancers on DNA sequences and RNA-binding proteins, which—as the name suggested—are both crucial regulatory elements in biological processes. Current high-throughput sequencing techniques for selecting candidate binding targets for certain TFs are restricted by the low efficiency and high cost . ResearcMany existing deep learning methods approach TFBS prediction tasks through convolutional kernels. Alipanahi et al.  The effects of mutations are usually predicted by site-independent or pairwise models, but these approaches do not sufficiently model higher-order dependencies. Riesselman et al.  , such as FASTA , BLAST , or PSI-There are also some works based on available gene function annotation vocabularies  to perfThe protein Secondary Structure (SS) refers to the local spatial structure formed by the interaction between nearby stretches of a polypeptide chain. The protein SS encodes information for predicting the biophysical properties of amino acid residues, higher-level protein structures , protein functions and evolution. It is traditionally described by either a three-state model  or an eiBefore deep learning became popular for protein SS prediction, machine learning approaches including probabilistic graphical models ,221,222,Emergent deep architectures for protein SS prediction have been widely explored with more prior knowledge and various features available. Faraggi et al.   has been proposed in recent years to utilize the power of large-scale, transformer-based language models. Rives et al.  introducA protein contact map is a binary 2D matrix denoting the spatial closeness of any two residues in the folded 3D protein structure. Predicting residue–residue contact is thus crucial to protein structure prediction, and it has been studied using shallow neural networks . Recent In recent years, ResNet  has beenhttps://www.rcsb.org/ (accessed on 10 September 2023)) keeps growing, it only accounts for a small proportion of currently sequenced proteins [The prediction of protein tertiary structure has proven crucial to human understanding of protein functions  and can proteins . Thus, aTwo essential challenges in protein structure prediction include the sampling and the ranking of protein structural models . QualityModern tertiary structure prediction systems typically pipeline functional modules that (i) query MSA for target protein sequence; (ii) predict the contact map or residue–residue distance, and (iii) reconstruct a 3D structure based on predicted contact map under energy and physical constraints. The quality of these MSA-based systems can depend sensitively on the performance of the involved contact map prediction models . MULTICOThe AlphaFold  and AlphA more serious limitation with AlphaFold2 is the inability of predicting novel structures due to its dependence on known MSA features. ProtLM models which are independent of MSA features overcome this limitation trivially and have shown great potential in novel structure prediction. Lin et al.  introducWith the discussion of the successes of applications of deep learning in genomics, now we proceed to discuss some current challenges. As deep learning models are usually over-parametrized, the performance can be conditional if the models are not appropriately designed according to the problem. There are multiple worthwhile considerations and techniques involving model architectures, feature extraction, data limitation, etc., which help deep learning models better approach genomics. Here, we briefly discuss some current challenges that deserve attention and several potential research directions that might shed on light the future development of deep learning applications in genomic research.An inevitable challenge of transferring the success of deep learning in conventional vision or text data into genomics is raised due to the nature of the genomic data, such as the unavailability of true labels due to the lack of knowledge of the genetic process, the imbalanced case and control samples due to the rarity of a certain disease, and the heterogeneity of data due to the expensiveness of large-scale data collection. While privacy is always a notable challenge in genomic data sharing and research , dealingLarge-scale biological data gathered from assorted sources are usually inherently class imbalanced. Take epigenetic datasets, for example: there are in nature much fewer DNA methylated region (DMR) sites than non-DMR sites . It is aIntuitively, integrating diverse types of data as discriminating features will lead to more predictive power of the models. For example, Liu et al.  (PEDLA) It pays off to manage to utilize the data of multiple views; although merging the information from various data sources challenges the models that could sufficiently integrate them, this effort might provide more information with a great chance. Also, critical feature processing has proven helpful to facilitate data integration . A revieThe data in most genomic applications involving medical or clinical experiments are heterogeneous due to population subgroups or regional environments. One of the problems of integrating these different types of data is the underlying interdependencies among these heterogeneous data. Covariance is sometimes confounding and renders the model prediction inaccurate.The Genome-Wide Association Study (GWAS) is an example where both population-based confounders (population subgroups with different ancestry) and individual relateness produce spurious correlations among SNPs to the trait of interest. The GWAS investigates the entire genome to identify SNPs associated with diseases or traits of interest, enabling people to predict the genetic predisposition of a disease and cure it by targeting corresponding SNPs. The challenges are twofold: one is to develop more expressive casual models, and the other is to address confounding factors across heterogeneous groups . Most exFrom the methodology perspective, several deep learning methods that are not designed exclusively for confounder correction, such as the domain adversarial learning , select-Deep learning that performs automatic feature extraction saves great efforts in choosing hand-engineered features. With the vast scale of typical genomics data and the complexity of sequences, manual features, such as the GC content of a DNA sequence, carry little distinguishable information compared to automatic feature extraction powered by deep learning, which is able to interpret more subtle features from the data .Torng anMathFeature, which implements 20 mathematical descriptors and 17 convential descriptors used to numerically encode long gene sequences.Although deep neural networks are capable of capturing high-level features from low-level input data, the entangled geometric complexity and biological complexity underlying the 3D biomolecular structure of the protein have greatly hindered the generalization of deep learning to tremendous biological problems . TechniqBy the conceptual analogy of the fact that humans communicate through languages, biological organisms convey information within and between cells through information encoded in biological sequences. To understand this language of life, Asgari and Mofrad  designedAnother feature representation technique was proposed by Hao et al.  on canceGenomics is a challenging application area of deep learning that encounters unique challenges compared to other fields such as vision, audio, and text processing, since we have limited abilities to interpret genomic information but expect from deep learning a superhuman intelligence that explores beyond our knowledge. Yet deep learning is undoubtedly an auspicious direction that has constantly rejuvenated and moved forward genomic research in recent years. As discussed in this review, recent breakthroughs of deep learning applications in genomics have surpassed many previous state-of-the-art computational methods with regard to predictive performance, although they slightly lag behind traditional statistical inferences in terms of interpretation.Current applications, however, have not brought about a watershed revolution in genomic research. The predictive performances in most problems have not reached the expectation for real-world applications and neither have the interpretations of these abstruse models elucidate insightful knowledge. A plethora of new deep learning methods are constantly being proposed but await artful applications in genomics. By the careful selection of data sources and features or the appropriate design of model structures, deep learning can be driven toward a bright direction which produces a more accurate and interpretable prediction. We need to bear in mind numerous challenges beyond simply improving predictive accuracy to seek essential advancements and revolutions in deep learning for genomics.


This article examines the relationship between cognitive processing and mastery. While scholars have called for the integration of sociological and cognitive analyses of mastery, sociological research has focused almost exclusively on mapping its social correlates. As a result, sociologists have relied on untested and underspecified assumptions about cognition to explain the efficacy of mastery. Taking an interdisciplinary approach integrating research on mastery, dual-process models of cognition, and intersectionality, we specify and test the hypothesis that deliberate thinking dispositions are associated with a greater sense of control over one’s life chances and assess whether this relationship varies across different intersections of social positions. Regression results from survey data in a diverse student sample (N = 982) suggest a positive correlation between deliberate cognitive style and personal mastery. However, results from a quantitative intersectional analysis demonstrate that this relationship does not hold for East Asian women. Among the most robust findings from research on stress processes is that mastery—the extent to which people regard their life chances as being under their own control instead of being fatalistically ruled :5—holds  We take seriously Mastery’s considerable consequences for health and well-being continues to provoke a wide range of social scientific research aimed at better understanding where it comes from and how it develops. As To investigate this relationship, we engage with sociological and psychological research on dual-process models of cognition . This sc We suggest that deliberate cognitive styles support mastery because greater motivation to engage in effortful cognitive activity facilitates more successful problem-solving, which in turn provides a greater sense of control. However, we suggest that this relationship diverges for East Asian women in our sample as a result of both institutional discrimination (in which their deliberate thinking efforts are disregarded) and issues of institutional fit (where their deliberate thinking is rooted in a chronic state of unsettledness), both of which produce a negative association between deliberate thinking and mastery. These analyses offer a baseline to inform future research testing the causal linkages between mastery and dual-process models of cognition for the stress and health of marginalized intersectional groups.We test this hypothesis with (to our knowledge) the only publicly available data measuring both mastery and cognitive styles: the Peel Social Lab Survey. Then, drawing on recent advances in the quantitative application of intersectionality theory , we testMastery is the generalized belief or understanding individuals hold regarding their ability to manage the circumstances of their lives. Mastery is closely related to constructs such as locus of control , self-ef For example, Asians report lower average sense of control than non-Asians  execute autonomously, make minimal demands on working memory, and are often fast, intuitive, and unconscious. Type 2 processes (what we refer to as deliberate cognition) require controlled attention, place greater demands on working memory, and are often slower, deliberative, and conscious  learned. Based on meta-analyses across multiple measures of cognitive style, men are more analytical and less intuitive than women , educatineed for cognition: stable individual differences in the motivation to engage in effortful cognitive activity  and a negative interaction for those from marginalized groups .To test this hypothesis requires an analysis of the social locations that potentially condition the relationship between deliberate cognition and mastery. Informed by the extensive social research on the importance of intercategorical statuses  for mastery as we reviewed previously, we take an explicitly quantitative intersectional analysis to consider how the focal relationship may be contingent on cross-cutting identity categories . RecogniThe present study tested the hypothesized association between cognitive styles and mastery through a secondary data analysis using the only publicly available data measuring both mastery and cognitive styles: the 2019 to 2020 first wave of the Peel Social Lab Survey (PSL). A unique strength of this study is the large sample from a diverse Canadian university. Respondents were two cohorts of first-year undergraduates who, encouraged to participate with one bonus percentage in a large introductory course, indicated informed consent and then answered a battery of questions. Pooling these cross-sectional cohorts increased statistical power to provide for the testing of both the hypothesized association linking cognitive styles and mastery and an interrogation of potentially unique differences across self-reported gender and ethno-racial identities. To reflect which cohort the respondents participated in, all models included a dummy variable. The order in which respondents answered questions within each section was randomized to mitigate question order bias. The survey responses were anonymized, and respondents who did not consent to their information being used for research purposes were removed from the public release prior to this secondary data analysis. Variables truncated or removed in the process of anonymizing the data for public release held no theoretical relevance for the hypotheses under investigation. After missing data were handled, the analytic sample consisted of 982 respondents.These data contained three items from the Our focal independent variable was three items from the Need for Cognition (NFC) scale. This is a validated self-report measure of preferences for analytical processing that predicts behavior in ways that are consistent with dual-process theories . For exaUsing only a few items to measure deliberate cognitive style is not uncommon. For example, the REIm–13, a validated, shortened version of the Rational-Experiential Inventory, measures rational thinking using a four-item subscale see . RespondRespondents also reported several ascribed and achieved statuses known to be associated with social differences in mastery. Gender was coded as 1 for men and 0 for women. Race-ethnicity was coded with three dummy variables: South Asian (reference), White, and East Asian. Employment status was coded as 0 for respondents not working and 1 for respondents currently working for pay. The education level of caregivers contrasted at least one caregiver in the household having attained a university degree or higher coded as 1 with both caregivers having less than a bachelor’s degree when respondent was 16 years of age (coded as 0). Cohort was coded as 1 for the 2020 cohort.The analytic advantage of the quantitative intersectional approach is an explicit focus on the myriad social differences linked with ascribed statuses. Briefly beginning with descriptive statistics in The highly diverse student population from which this sample was drawn provided for the potential interrogation of our hypothesized focal relationship across 16  unique intersectional statuses. However, small cell sizes for several of these groups when considering ethno-racial and gender status simultaneously resulted in low statistical power and connoted the inability to detect relatively small group differences, producing imprecise estimates. As a result, we focused on the largest three ethno-racial groups (which have greater than 100 respondents for each gender in this study): White people, South Asian people, and East Asian people. By doing so, we aimed to provide reliable evidence on the social stratification of the hypothesized relationship while taking into account the challenge of small subgroup sizes for quantitative intersectional analyses. Including the full prism of intercategorical statuses did not substantively alter the results presented, nor were three-way interactions for the excluded statuses significantly different from the main association presented herein.https://osf.io/ryhtx/). This similarity and the importance of parsimoniously visualizing the three-way interactions required to test our second hypothesis guided our decision to use complete case analysis instead of FIML or MICE to handle missing data. Additionally, robust standard errors were used in all models to adjust for heteroskedasticity and the clustering of two cohorts . This direct association accounts for approximately 2% (R2 = .021) of the overall variation in mastery for these people. Models (in replication package) that exclude cognitive style and only include controls explain less than 1% (R2 = .008), and models that adjust for cognitive style and the control covariates without intersectional means or slopes increase that to about 3% (R2 = .029). These two alternate model specifications suggest that the hypothesized focal association would make less of a contribution for explaining inequalities in personal mastery without the explicit investigation of quantitative intersectional differences.b = .19, p < .01). People who identify as White or South Asian men do not report a detectibly different mean mastery, or association, from those in this reference group. This suggests that reporting a higher need for cognition also corresponds with a higher belief in one’s personal mastery for most people in this sample. Adjusting for theoretically informed controls in Model 3 does not change this pattern. Consistent with stress and health literature, social structural location predicts differences in mastery. Overall, South Asian women (the reference group) who report a more deliberate cognitive style also report higher levels of personal mastery  when reporting the same need for cognition. Along with this significant mean difference, East Asian women with a higher need for cognition also report lower levels of personal mastery . Interactions and mean differences for unique intersectional statuses are not detectibly different from the focal association for five of the six intercategorical locations.2 in our final model is comparable with other studies that predict mastery when models adjust for gender and ethno-racial statuses (see 2 in our final model is modest, we suggest that the present study still usefully contributes to understanding the social patterning of mastery across intercategorical statuses given our a priori hypothesizing from the literature and the limited range of variables available in these data. Many of these statuses, however, are not detectably different from the focal association.Interestingly, and consistent with intersectionality theory, the challenges of veridically investigating cross-cutting social identities are worthwhile inasmuch as they provide additional purchase on understanding the prism of social differences. Doing so, as shown in uses see . AlthougDecomposing the association across intercategorical identities is an important step when taking the investigation of social differences in resources seriously, yet it is evident that quantitative intersectional scholarship requires relatively large samples due to the complexity of estimating many three-way interactions between the focal predictor, ethno-racial statuses, and genders. This may also be behind the lack of significant gender differences predicted by seminal studies of mastery. However, and as suggested by recent literature, it is also possible that these genders are growing less unequal in regard to personal mastery. Finally, the importance of caregiver education at home when respondents were age 16 and respondent employment status are not significant in these models.The relationship between mastery, stress, and health is well established within the social sciences. While there are decades of social science research establishing the sociological correlates of mastery, this scholarship has relied on relatively untested assumptions regarding the cognitive processes underpinning mastery as an explanatory mechanism for its causal efficacy. To remedy this, we follow We find a mostly positive relationship between deliberate cognitive style and mastery. There is some evidence, however, that this relationship is contingent on intersectional social identity. Regression estimates indicate that people reporting a more deliberate cognitive style on average also report greater belief in their personal mastery. By contrast, when reporting greater need for cognition, East Asian women also report lower personal mastery. Quantitively applying intersectionality theory approximately doubles the, albeit modest, coefficient of determination to increase the explained variation in mastery. Taken together, these findings both suggest that mastery is related to cognitive processing and that there are social patterns in the cognitive origins of mastery.Drawing on research on both mastery and cognitive styles, we argue that the positive association between deliberate cognitive style and mastery is grounded in successful problem-solving because people with deliberate cognitive styles should have richer histories of successful problem-solving, which in turn should increase their sense of control see . HoweverBridging intersectionality  with psyInstead, we suggest that our findings are best explained by applying both cultural and intersectional analysis more narrowly to the institutional context in which these data were gathered, focusing specifically on why increased deliberate thinking decreases the sense of control of East Asian women in the University of Toronto. Although intersectionality is often seen as shaping entire social systems, it can also be used to understand how multiple axes of oppression operate within specific institutional contexts (although approaches to intersectionality vary in the degree to which they grant institutional primacy in explaining the production of social inequalities; see East Asian students are often lauded for their academic performance; however, they face a variety of barriers to success in North America universities. For our purposes, the most significant institutional barrier is the bias toward talkative students and “active participation” in Western education see . This diWe suspect that this has specific and disproportionate impacts on East Asian women. For one, their sense of self and identity is often affected by cultural notions that passivity and inability constitute feminine virtues, which may inhibit them inside and outside the classroom :12. PartIn realizing that their existing skills, dispositions, and strategies are ill-suited to context of North American universities, we expect that East Asian women engage in a reflective and cognitively costly search for new cultural models with which they may reorganize their behavior . East AsThis suggests that there are two interrelated pathways through which increased deliberate thinking leads to decreased mastery for East Asian women. First, as the result of East Asian women’s particularly careful and contemplative approach to class participation and the disregard they face from fellow students and professors, the lack of reward or recognition they receive for their reflective efforts in class engenders a greater sense of futility with regard to their sense of self-efficacy. Second, the recognition that their passive orientation is misaligned with the reward system in North American universities produces a state of mild “crisis,” which initiates an active and anxious search for explicit models from which to develop new strategies of action. The more prolonged this reflective search, the more intense their experiences of negative affect  and theiWhile we focus primarily on mastery, the findings from this study also have implications for important debates about culture and cognition. Several sociologists have argued that our beliefs and behaviors are grounded primarily in unconscious dispositions that operate through automatic cognition, discounting the role of reflective thought . ConversAlthough to our knowledge this study uses the best publicly available data to test the proposed lines of inquiry, there are several limitations to briefly note. Because these analyses are based on cross-sectional data, we cannot make definitive causal claims. Future prospective research with multiple observations per person would increase confidence in the causal nature of the focal relationship in this study . FurtherTo conclude, although sociological research depicts increased cognitive effort from those with higher levels of mastery, this association has generally been assumed rather than specified and tested directly. Bridging research on mastery with insights from dual-process models of cognition, we expected that deliberate cognitive styles were significantly associated with mastery. Drawing on theoretical tenets and methodological developments from intersectionality, we then hypothesized that this focal relationship would be conditioned on social positions of power. Comparative evidence from cross-cutting ascribed statuses indicates that for most, but not all, of the distinct ethno-racial and gender intersections in this sample, people reporting a more deliberate cognitive style also, ceteris paribus, endorse stronger beliefs in their personal mastery. This study suggest that cognitive styles hold important implications for researchers interested in mastery and its effects on stress and health inequality.


Interestingly the correlation between cancer and AD appears to be purely negative/inverse, suggesting that susceptibility to one disease may be protective against the other   appear mechanistically distinct, i.e., the former acquires mechanisms to resist and evade cell death, while the latter is characterized by progressive cellular demise and degeneration in specific neuronal populations. Nevertheless, there is an ongoing debate about the inverse epidemiologic relationship between cancer and NDD. A substantial number of cancer survivors have a lower risk of developing NDD, particularly Parkinson's disease (PD) and Alzheimer's disease (AD), whereas less malignancies are observed in PD and AD patients. Several biological hypotheses have been put forward  are associated with both familial and sporadic PD. There is plethora of knowledge indicating the “inverse or overlapping—mechanisms, another avenue that remains to be explored is the identification of distant molecular contributors involved in these two entities. A prerequisite for such possible causative contributors should be their ability to play a dichotomous functional role in cancer , having open access to the epigenetic landscape/chromatin machinery  and a strong tendency to cross-talk with other non-cancerous hallmarks. Interesting candidate are the ubiquitin C-terminal hydrolases , a subfamily of deubiquitinating enzymes, which we have recently shown to be involved in both cancer and NDD (Sharma et al., in vitro, constituting a pathological hallmark of AD (Bhattacharya et al., Beyond the aforementioned—disease onset point” by understanding the aforementioned overlapping/inverse molecular mechanisms. Given the rapid development of sequencing technology it can be envisioned that defining genotype of sporadic and familial cases across populations may improve insight into the shared genetic architecture connecting disease specific phenotypes, ranging from cancer to NDD. Consequently, such approaches will also help identify aging-related exponential accumulation of mutations that may possibly be correlated with the proteins/proteolytic fragments released by degenerating neurons in order to develop advanced novel therapies.It is widely accepted that NDD start long before clinical symptoms (Sharma et al., All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Functional MRI is a well-established tool used for pre-surgical planning to help the neurosurgeon have a roadmap of critical functional areas that should be avoided, if possible, during surgery to minimize morbidity for patients with brain tumors . This article reviews the locations of secondary language centers within the brain along with imaging findings to help improve our confidence in our knowledge on language lateralization. Brief overviews of these language centers and their contributions to the language networks will be discussed. These language centers include primary language centers of “Broca’s Area” and “Wernicke’s Area”. However, there are multiple secondary language centers such as the dorsal lateral prefrontal cortex (DLPFC), frontal eye fields, pre- supplemental motor area (pre-SMA), Basal Temporal Language Area (BTLA), along with other areas of activation. Knowing these foci helps to increase self-assurance when discussing the nature of laterality with the neurosurgeon. By knowing secondary language centers for language lateralization, via fMRI, one can feel confident on providing neurosurgeon colleagues with appropriate information on the laterality of language in preparation for surgery. A maximal safe resection is the recommended first line treatment for gliomas. Awake craniotomies may result in reduced postoperative neurological deficits, due to the improved identification of functional eloquent cortices in order to better guide surgical resection . A numbeAs a review article we reviewed current literature on language activation areas within the brain along with providing our own working knowledge of these areas and their importance in presurgical mapping for brain tumor patients.The traditional method for calculating a language laterality index (LI) is a subtraction of the total number of activated voxels in the right hemisphere from the total number of activated voxels in the left hemisphere, divided by the total number of activated voxels in both hemispheres (LI = (nVxL − nVxR)/(nVxL + nVxR)). This results in a value ranging from −1 to 1 in which a negative number indicates a relatively greater proportion of activated voxels in the right hemisphere and a positive number indicates a relatively greater proportion of activated voxels in the left hemisphere. Voxels in the primary visual cortex, presumed not to reflect language processing per se, are typically masked out or excluded from the LI calculation. Doing so has been shown to increase concordance between fMRI estimates of language laterality and the Wada procedure . As disc2 administration and breath hold tasks have been used to quantify regional variations in cerebrovascular reactivity as an adjunct to task-based fMRI to map regions where lesion associated neurovascular uncoupling may be contributing to false negative errors [As discussed above, use of a single statistical threshold for the calculation of the LI may bias the interpretation of laterality and has been implicated in findings of pseudo-reorganization in tumor cases. Specifically, as lower thresholds are used to detect activation, more nondominant hemisphere voxels tend to become statistically significant, resulting in a less strongly lateralized index . Calculae errors .Task selection and ROI selection may also play an important role in the reliability of LIs in the setting of tumor. As mentioned previously, differences in the tasks used in each study impacts the interpretation of language laterality and the most reliable and robust laterality interpretation takes into account several different tasks and their nodes of overlap . Pillai Patient factors also play a significant role in the interpretation of LIs. One of the most widely used predictors of language laterality is handedness, typically assessed using the Edinburgh Handedness Inventory . A majort-test score, or the LI. Instead, each of these indices are considered in conjunction with patient factors as described above, given that the clinical utility of each is not in determining with perfect accuracy each area within the language map. Rather, our goal is to create the most sensitive map possible while retaining navigational utility and to relay to our neurosurgeon colleagues how confident we are in the data presented. In our interdisciplinary team, the neurosurgeons understand this also and take the information we present them as a rough roadmap for their interoperative awake mapping, which is considered the gold standard.With the above, a background of tools is provided for fMRI and gaining confidence with determining language lateralization. As mentioned above, individual tasks have varying reliability for certain areas of the language network and careful consideration should be made when choosing tasks. With our institution, though, we have decided to always perform the same three language tasks of sentence completion, word generation, and object naming. The reason for this being is that it ensures standardization between operators, covers a large area of the complete language network, and allows for an overlap of tasks for further confidence in the true predictive value of the language centers. Additionally, in clinical practice we do not set a specific threshold for the Edinburgh Handedness Inventory (EHI), the In addition to identifying language laterality, lesion-to-activation distance is another metric provided by preoperative fMRI that predicts postoperative functional outcomes and is useful for guiding intraoperative electrocortical mapping ,42. ThisLesion to activation distance has also been shown to be related to preoperative language performance. Riley and colleagues (2022)  examinedLesions to Broca’s and Wernicke’s areas are associated with postoperative deficits and should be carefully localized preoperatively and mapped intraoperatively. However, as will be discussed in the next sections, it is essential to consider the integrity of the whole language network especially in light of evidence that significant postoperative language deficits can arise from damage to language areas typically considered ancillary. Furthermore, evidence suggests that we must reconsider whether resection of cortex in these primary language areas is truly associated with the aphasias typically ascribed to these regions.The left inferior frontal gyrus (IFG), comprised of Brodmann areas 44 and 45, gained its moniker Broca’s area from Paul Broca who observed speech production deficits in two historic stroke cases. Subsequently this region of the IFG became synonymous with speech production, despite accumulating evidence to the contrary . NotablyBroca’s area is a functional region with considerable intraindividual differences in its anatomic location and morphology , which hWith the aid of electrocortical mapping, the pars triangularis and pars opercularis regions of the left inferior frontal gyrus  have been found to be a safe corridor for the resection of insular lesions ,58. No lWernicke’s region is ill-defined anatomically, consisting of the superior temporal gyrus in Wernicke’s initial account , but alsDue to the severe deficits associated with damage to Wernicke’s area, this functional region has long been considered inoperable in the context of epilepsy or tumor surgery. However, a number of individual case studies have demonstrated successful glioma resections in Wernicke’s area with awake intraoperative mapping with direct electrocortical stimulation. In a case series by Sarubbo and colleagues, intraoperative mapping for tumor resection with direct electrocortical stimulation of superior temporal gyrus and supramarginal gyrus produced anomias and semantic paraphasias . In one Preoperative functional neuroimaging is an essential tool providing an initial map of the potential language eloquent cortex, especially within a functional region that is so highly variable in terms of its anatomic location such as Wernicke’s area. Presurgical fMRI paradigms have relied on sentence completion to localize Wernicke’s area in presurgical tumor cases, as sentence completion is more reliable than word generation or category naming in eliciting Wernicke’s activation . In the The reorganization of function may also occur in the setting of a prior resective surgery for glioma, allowing for more complete resection later, even when eloquence is identified within the tumor margins. In one case report, a patient underwent an initial awake resection of a left temporal glioma, which was incomplete due to positive language mapping sites during this initial surgery . A seconThe ability to engage in complex daily social and professional functions is an emergent property of numerous discrete cognitive functions and their interactions. More modern connectome models of brain-behavior relationships argue against a modular localizationist model in which brain regions have discrete specialized functions in a one to one map . AttendiThe dorsolateral prefrontal cortex (DLPFC) is located in the middle frontal and superior frontal gyri, comprised of the Brodmann areas 46 and 9 . The DLPIntraoperative direct electrocortical stimulation of the DLPFC has elicited working memory disturbance , anomiasFunctional MRI studies of the DLPFC contributions to language function have largely investigated its shared role in executive functioning/working memory and language control processes, especially for bilingual and multilingual individuals. Meta-analytic evidence suggests that the DLPFC cooperates with a network of other regions including the pre-supplementary motor area, the anterior cingulate cortex, and the precuneus during language switching for bilingual individuals . DLPFC aThe Graphemic Motor Frontal Area (GMFA) or Exner’s Area sits posterior to the DLPFC in the middle frontal gyrus at the most inferior part of Brodmann area 6 . It is bIntraoperative mapping and postoperative cognitive performance data from patients who have undergone surgery has been more equivocal about the precise role of GMFA, potentially due to intraindividual differences in functional anatomy and the location of the tumor. Direct electrocortical stimulation of GMFA has produced anarthria , anomia Tasks used to elicit fMRI activation in GMFA have included sentence completion, letter strings, object naming, verb generation, naming from written description, and naming from aural description ,94,95,96The Frontal Eye Fields (FEFs) are situated just posterior to the DLPFC in the superior frontal gyrus and are generally thought to be comprised of the Brodmann area 8 . HoweverTo our knowledge, no reports have detailed postoperative outcomes for tumor resection of language dominant FEFs. In one case report of preoperative fMRI and awake resection of low-grade glioma in right FEFs, self-paced symmetric horizontal saccades were used to map the activation of FEFs on fMRI . PresurgJust anterior to the supplementary motor area  is a functional region termed Pre-SMA or language SMA, typically defined as the medial aspect of Brodmann area 8 and the aIntraoperative direct electrocortical stimulation of the language SMA during awake tumor resection has resulted in speech arrest ,112. In Functional MRI activation in the language SMA has been elicited by semantic decision making , languagThe inferior parietal lobule (IPL), often called Geschwind’s Area, sits just inferior to the postcentral gyrus and is inferior and lateral to the superior parietal lobule as divided by the intraparietal sulcus . It is cDirect electrocortical stimulation of the IPL has produced alexia, anomia, semantic paraphasias, and phonemic paraphasias during word reading and object naming tasks during awake glioma resection . In thisp < 0.005). When using activations for both tasks together, along with their overlap, prediction of positive intraoperative mapping sites improved substantially . The prediction of positive mapping sites also varied by the threshold chosen, with a less restrictive threshold resulting in a higher sensitivity when considering both tasks combined . The authors emphasize that while this study highlights the predictive utility of fMRI, it also shows the necessity of direct electrocortical stimulation mapping, as fMRI alone cannot identify language eloquent cortex with a sufficient enough sensitivity to avoid postoperative deficits.Presurgical fMRI paradigms used to identify language eloquent cortex prior to the resection of gliomas in the IPL have included naming, verb generation, phonemic fluency, and rhyming ,135,136.The basal temporal language area (BTLA) refers to a functional region within the posterior and inferior temporal lobe that has been implicated in language function since at least the late 1800′s . While tInitial studies of the surgical resection of this region suggested no significant impairment arising from its surgical removal, though more recent studies suggest more significant functional consequences. Pouratian et al. (2003) reported a case involving the surgical resection of the dominant fusiform gyrus in a patient receiving preoperative fMRI, pre- and post-operative neuropsychological evaluation, and intraoperative functional mapping . fMRI acThe fMRI task most commonly used to elicit BTLA activations  is some In the era of fMRI, one now has the chance to help preserve critical language functions during brain surgery for tumors. Our review of preoperative neuroimaging and postoperative language outcomes for surgical resection in eight nodes of the language system shows that significant language deficits can arise from damage to regions not circumscribed to traditional Broca’s and Wernicke’s areas. By knowing these nodes when performing fMRI, one can gain confidence in knowing what a patient’s true laterality is. Furthermore, this review highlights the importance of presurgical mapping of language areas traditionally presumed secondary or ancillary. fMRI has reasonable concordance with the direct intraoperative electrocortical mapping in each of these regions and remains a useful clinical tool for communicating informed consent about the functional risk to the patient, for planning the surgical approach, and for the selection of intraoperative mapping tasks. Additionally, while fMRI should not be used in isolation with the available direct awake electrocortical stimulation mapping of the language eloquent cortex, it provides adequate functional mapping for avoiding postoperative functional deficits when awake methods are not feasible .Furthermore, the integrity of the language network as a whole must be considered for presurgical planning, including the essential language white matter tracts that connect among language eloquent cortical regions. As discussed previously, a number of studies have demonstrated language deficits resulting from the disconnection of cortical regions due to damage to underlying white matter tracts, even if language eloquent cortical areas are spared ,81,120. Task based fMRI that focuses primarily on identifying nodes of activity remains the most extensively validated method for presurgical language mapping. This method does not characterize the integrity of the language network as a whole, per se. This review has focused primarily on the importance of characterizing ancillary language regions with the understanding that these regions contribute substantially to the language network. Though it has not been within the scope of this review to discuss metrics of quantifying the integrity of the language network itself, these metrics are an important future direction of research for clinical use. Briefly, functional connectivity analyses, both through task based and resting state fMRI, are methods of measuring the functional cooperation among networks of eloquent regions. Gliomas have been shown to be associated with disruption to both local and broad functional networks . Decreas


This curriculum is designed for resident physicians at all levels of training who have an interest in HEMS.This curriculum is designed to run over a 28-day period.Helicopter emergency medicine services play a critical role in patient transport, and resident physicians may often encounter patients transported by HEMS programs. Residents, and emergency medicine residents in particular, are being offered more opportunities to gain flight experience with HEMS programs; however, these experiences may be highly variable. These inconsistencies in training experiences may lead to incomplete understanding of HEMS systems and patient care performed during flight.The primary objective of this course is to present a standardized curriculum which allows learners to gain understanding of HEMS systems and retrieval medicine while practicing safety in the aviation environment.The educational strategies used in this curriculum include hands-on training with senior flight staff, asynchronous learning via access to a curated reading “library,” and in-person learning via ride-along experience on the aircraft and program operational meeting attendance.This curriculum was introduced at the authors’ institution and was completed by 11 rotating resident physicians, primarily PGY-2 and PGY-3 emergency medicine residents. Learners completed free-form feedback forms as well as a numerically graded post rotation survey. Learner feedback was used to identify areas where additional instruction was necessary and make changes to optimize learner flight experience.It was helpful for me to spend some time in the airway [and procedure] lab and go through the shift topics.”The curriculum was graded by learners on a 5-point Likert scale. The statement of “My overall educational experience during the rotation met my expectations and the learning objectives outlined at the beginning of the rotation” received an average score of 4.7 based on 1-Disagree to 5-Completely agree. The statement “The longitudinal curriculum contributed to my learning” averaged 4.5. A score of 4.9 was given for the statement “I found the rotation to be of use in my emergency medicine training.” Free-form feedback was also solicited by learners and included comments such as “As evidenced by the learner feedback and survey results, the curriculum was effective in meeting the designed educational objectives, and learner feedback was largely positive in nature. Utilizing dedicated daily teaching topics was key to providing a standardized learning experience and ensuring that education progressed without reliance on flight call volumes.Helicopter aviation safety, HEMS crew member operations, retrieval medicine, advanced trauma care, advanced airway management, ventilator management, HEMS program operations. Learner Audience:Interns, Junior Residents, Senior ResidentsLength of Curriculum:This curriculum is designed to run over a 28-day period but could be adjusted based on timing needs.Topics:Helicopter aviation safety, HEMS crew member operations, retrieval medicine, advanced trauma care, advanced airway management, ventilator management, HEMS program operations.Objectives:The goals of this curriculum are:Learners will show understanding of, and participate in, safe practices in and around the aircraft.Learners will demonstrate understanding of operations as a HEMS crew member as well as having knowledge of HEMS transport protocols.Understand pre-hospital triage and emergency care deliveryPractice rapid assessment and stabilization of critically ill and injured patients prior to and during transportLearn to communicate effectively with medical staff from transferring facilities in the coordination of referral patientsLearn to communicate effectively with all levels of EMS providersLearners will demonstrate knowledge of advanced topics and procedures relevant to critical care transport and retrieval medicine.Learn the indications and contraindications for commonly used blood products, intubation and sedation medications, and hyperosmolar agentsUnderstand methods for difficult airway management including adjuncts for endotracheal intubation as well as extra-glottic devices and surgical airwaysDemonstrate appropriate use of a cardiac monitor for defibrillation, cardioversion, and cardiac pacingLearn the approach to managing ventilated patients who are hard to ventilate or develop high peak airway pressuresLearners will develop an understanding of the functions of a HEMS program.Develop understanding of appropriate utilization of ground and air medical transportExplore the integration of ground-based EMS and helicopter EMS into a unified system of pre-hospital and inter-hospital providersLearners will understand a HEMS program from an operations perspective with a focus on EMS quality improvementThere are roughly 400,000 patients transported by helicopter emergency medical services (HEMS) annually in the US.Resident physician HEMS participation is becoming more common among emergency medicine residency programs,7We present a curriculum designed to allow learners the opportunity to learn advanced retrieval medicine, practice safety in the aviation environment, and observe the management functions of a HEMS operator. This program is designed to be replicated and adapted by a variety of emergency residency programs. Ideally this would allow programs without any formal HEMS curriculum to establish one and allow for better standardization of HEMS training across emergency medicine residency programs.There is currently no standardized HEMS curriculum in use by emergency medicine residency programs despite increasing involvement of resident physicians with HEMS. More concerning is that some training programs have no formal established curriculum, causing variability in experiences among learners. Ultimately this may have consequences on patient care and resident understanding of HEMS operations.The aim of this curriculum is to establish a structured, standardized model for residency programs offering HEMS experience to residents.Learners will show understanding of, and participate in, safe practices in and around the aircraft.Learners will demonstrate understanding of operations as a HEMS crew member as well as having knowledge of HEMS transport protocols.Understand pre-hospital triage and emergency care deliveryPractice rapid assessment and stabilization of critically ill and injured patients prior to and during transportLearn to communicate effectively with medical staff from transferring facilities in the coordination of referral patientsLearn to communicate effectively with all levels of EMS providersLearners will demonstrate knowledge of advanced topics and procedures relevant to critical care transport and retrieval medicine.Learn the indications and contraindications for commonly used blood products, intubation and sedation medications, and hyperosmolar agentsUnderstand methods for difficult airway management including adjuncts for endotracheal intubation as well as extra-glottic devices and surgical airwaysDemonstrate appropriate use of a cardiac monitor for defibrillation, cardioversion, and cardiac pacingLearn the approach to managing ventilated patients who are hard to ventilate or develop high peak airway pressuresLearners will develop an understanding of the functions of a HEMS program.Develop understanding of appropriate utilization of ground and air medical transportExplore the integration of ground-based EMS and helicopter EMS into a unified system of pre-hospital and inter-hospital providersLearners will understand a HEMS program from an operations perspective with a focus on EMS quality improvement(See curriculum chart)Our institution has a long history of offering emergency medicine residents flight opportunities, and our curriculum had historically been an organically grown process. However, the most recent iteration presented here went into effect in 2018. For programs looking to implement this curriculum, we would recommend instating this as a complete bundle because piecemeal implementation may lead to inconsistent experiences by learners.Many learners have limited aviation experience, and safety should be a top priority during training. Residents should be outfitted with necessary equipment for safe flight operation  via either personal or program supplies. Learners should also feel comfortable working around the aircraft and demonstrate safe operations to the pilot or a crew member prior to beginning flight experiences.Prior to flight, learners should also be given instruction in basic flight crew tasks such as usage of radios, loading and unloading of the stretcher/litter, and usage of the cardiac monitor with the intention of functioning as a full member of the team. They should be given access to any available protocols which guide team performance. Learners will attend all crew and program briefings and be an integrated member of the team.Learners will gain clinical knowledge via ride-along experiences with the flight crew. At our institution, learners are scheduled for twelve, 12-hour shifts. Flight experience will include assessment and stabilization of critically ill and injured patients in the prehospital setting, as well as the continued care of critically ill patients during intra-facility transport. Learners should be encouraged to be involved with patient care to the level of training of the learner and within comfort of the HEMS crew. Involvement in patient care will also require interfacing with ground-based EMS in pre-hospital settings and hospital medical staff during intra-facility transports. A HIPAA compliant case log should be maintained to aid with case review and with tracking flight experience. Learners will be given feedback at the end of each shift by means of a dedicated post-shift feedback card which is filled out by the medical staff they are working with that day.It is hoped that during these flight experiences learners will be exposed to a variety of critical care circumstances including trauma, cardiac, neurologic, and airway emergencies. However, daily topics covering these subjects are provided for discussion because there may be variability in the flight experiences between learners. If possible, training equipment for flight staff should also be made available to learners to gain hands on experience. At our institution this includes pediatric and adult airway mannequins for practicing airway management strategies. A ventilator with test lung is available for teaching and practice with ventilatory modes and strategies. Access to hemorrhage control devices such as tourniquets, pressure dressings, and combat gauze also provide practical experience. Learners are also provided access to a reading “library” which contains texts and literature relevant to the practice of transport medicine.15Learners can gain an appreciation for the non-flight operations of a HEMS program by observing management meetings. In our program we include learners in the following meetings:Education and Training, which focuses on planning didactics and continuing education for critical care transport (CCT) staffPerformance Improvement, which focuses on performance improvement activities including case review and utilization managementOperations, which focuses on implementation of policies, workflows, and practices related to clinical careQuality and Safety, which focuses on case review, improving the quality of clinical care, and reviewing safety incidents within our program and the aeromedical transport industryMorbidity and Mortality, which focuses on an in-depth review of transports performed by the program which involved extraordinary circumstances or learning opportunities for CCT staffAll Staff Meeting, which focuses on dissemination of any critical information to all employed staff members and allows for staff feedback on changes or issues which have arisenIt was helpful for me to spend some time in the airway [and procedure] lab and go through the shift topics.” As evidenced by the survey and individual feedback, learners clearly found value in the rotation and felt the curriculum helped contribute to rotator learning.Eleven learners have successfully completed the curriculum as presented in this publication. The curriculum was graded by learners on a 5-point Likert scale survey with “1” indicating learners disagree with the given statement and “5” indicating the learner is in complete agreement. It received an average score of 4.7 regarding the statement, “My overall educational experience during the Med Flight rotation met my expectations and the learning objectives outlined at the beginning of the rotation.” The statement, “The longitudinal curriculum contributed to my learning,” scored 4.5 based on the same scale. A score of 4.9 was given for the statement “I found the rotation to be of use in my emergency medicine training.” Feedback was also obtained via free-form feedback responses requested from individual learners upon completion of the curriculum, and included comments such as, “The curriculum also underwent review by our continuous residency improvement committee.


In the absence of targeted empirical evidence on effective clinical communication in inflammatory bowel disease (IBD), a broad overview of existing evidence on effective communication in healthcare and available recommendations for communication in telehealth is provided and mapped onto IBD research and practice.A narrative literature review was conducted using Pubmed and Scopus databases and snowballing literature search.relationship building strategies include communicating emotions, acknowledging and addressing patients’ hesitancy, and ensuring continued support. A particular recommendation regarding telehealth interaction is to avoid long stretches of talk. Effective informational strategies include facilitating and supporting information exchange and considering patients’ preferences in decision-making. In teleconsultations, clinicians should ask direct questions about patients’ emotional state, clarify their understanding of patients’ concerns and check patients’ understanding, address at least one patient-reported outcome when discussing the recommended treatment, and shorten the consultation where possible. Strategies for maximizing effective clinical communication in the spoken communicative mode include using infographics and simple language, and assessing adherence at the beginning of the consultation. For teleconsultations, clinicians are advised to allow patients to explain the reason for their call at the beginning of the teleconsultation, probe additional concerns early and before ending the teleconsultation, and be mindful of technical issues such as voice delays.Evidence-based Use of question prompt lists, decision aids, micro-lessons, and communication training interventions for clinicians could be beneficial in IBD care. Further research into the implementation of such interventions as well as clinical communication concerns specific to IBD is warranted. Yet research evidence on how communication strategies can improve clinical outcomes in IBD is scarce.Effective clinical communication is crucial for optimal patient care in inflammatory bowel disease (IBD),2 Audit recommendations repeatedly involved the relationship between patients and their IBD team, indicating the important role of clinician–patient communication in reducing this disease burden. A related survey of over 700 IBD patients in Australia4 that came out of the audit confirmed many of the audit’s findings from the patient’s point of view and highlighted some additional concerns relevant to communication. In particular, 51% of the respondents wanted to be more involved in decisions regarding their care.3 Half of all respondents registered as mildly, moderately, or severely distressed on psychological testing but only 32% of those in severe distress were receiving care from a mental health clinician.4 Correspondingly, 56% of the patients who were not asked about their mental health by their specialist or IBD nurse wanted to be asked about this. Many patients also reported in this survey that no contingency plans were in place, or had never been discussed with their IBD clinicians.3In Australia, a 2016 national audit of the provision and organization of IBD services revealed a significant disease burden which could be alleviated by appropriate and well-coordinated care.5–11 These results regarding the role of communication are consistent with reported patient priorities internationally, including wanting more information earlier in their disease course12 and wanting to discuss the effect of IBD on personal relations and the patient’s quality of life during consultations.7 The latter could also help explain why patients turn to sources outside conventional medicine for information and treatment of IBD.13–15 These results fit with our understanding beyond IBD that most complaints about doctors are made about communication, not clinical competencies.16–18Internationally, the small set of studies we have resonates with the Australian picture, indicating that communication between gastroenterologists and patients has an important role in addressing clinical concerns around improving disease management and patient self-management, and avoiding unnecessary hospitalizations.Existing research on how to address these concerns is limited. Targeted empirical evidence on effective clinical communication in specific types of healthcare encounters is key for making conscious and sustained changes.20 this narrative review provides an overview of existing evidence on effective clinical communication from the broader health communication literature. We use the results to consider and prioritize existing gaps in research and practice in IBD. Given the recent turn to telehealth visits in IBD and the likelihood that this practice will continue, we particularly wanted to include knowledge on effective communication in telehealth settings, even though the literature on this has a smaller empirical base. Thus, we sought answers to the following review questions:In the absence of evidence specific to IBD,What aspects of clinical communication have been shown to be associated with improved disease outcomes in chronic diseases?What evidence- or consensus-based recommendations are available on clinical communication in telehealth?How can this knowledge help identify priority points of communication intervention in IBD?A literature search was performed using Pubmed and Scopus databases from inception through December 2022. The following keywords were used to identify relevant publications in English through title and abstract screening first and full-text review subsequently:“chronic,” “communication,” “doctor-patient,” “clinician-patient,” “disease outcomes,” “patient-reported outcomes,” “telehealth,” “telephone consultation,” “video consultation,” and “telemedicine.”Reference lists of selected articles as well as subsequent citing publications of landmark articles were screened for additional relevant articles.Quantitative and mixed-methods studies that investigated associations between aspects of clinical communication and health outcomes or the effect of clinical communication on outcomes in chronic disease management were considered eligible. In addition, publications on telehealth were included if they made recommendations on clinician–patient communication in their results or conclusion sections regardless of study design. This looser selection criterion for telehealth was set because of limited evidence in this area despite increasing research on communication in telehealth since the COVID-19 pandemic began.Data extraction and synthesis were performed in Excel and NVivo.The bulk of research on communication between healthcare providers and healthcare consumers focuses on primary care, oncology, and end-of-life care. Studies that investigate communication in chronic disease management (excluding cancer care) constitute a considerably smaller share of the literature. Mapping this research to the IBD setting requires care but is a useful starting point for considering what we already know and where priorities lie.Results from empirical studies on the relation between communication style and health outcomes in chronic disease management are presented followed by a summary of work on clinical communication in telehealth. Due to limited availability of empirical work on communication in telehealth, best-practice recommendations as well as any available findings are included.21–24 such as shared understanding,25 patient recall,27 patient health-related behavior,28 and patient adherence29–33 .2 As such the findings should be used as a starting point for IBD service providers, clinicians, and researchers in their effort to promote patient involvement. Priority should be placed on the development and implementation of user-friendly consumer interventions that encourage an active patient role. One useful type of intervention to achieve this is the question prompt list which is a structured list of potential questions that a patient may want to ask their healthcare provider.49 In addition, consumer interventions that provide education on the different aspect of care, including relationships with the IBD service clinical team, can support patient involvement. Examples of such interventions include patient decision aids and micro-lessons. IBD patient decision aids50–52 not only provide information on the different treatment options for IBD but also promote a more agentive role for the patient. Micro-lessons are short recorded audio or video presentations on a single, tightly defined topic and have been shown to engage learners and reduce mental fatigue.53 An example of an IBD-specific micro-lesson is the Healthline IBD micro-lesson, the clinical usefulness of which requires further research.These finding are relevant in IBD where disease control, symptom management, quality of life, and healthcare utilization are important and where patient involvement should be a priority.38 By contrast, the following behaviors have been shown to be linked to or result in improved health and lower risk of illness, better utilization of evidence-based therapies, and reduced costs41–46:Looking at the physician’s communicative behaviors, more control of communication flow by the physician in the form of asking questions, giving directions, and interruptions has been shown to be related to poorer reported health.provision of information, in general, and provision of information regarding modifiable risk factors,support of patient co-production of clinical interventions,support of shared decision-making ,providing medical information in simple language,asking the patient to restate information or instructions to avoid misunderstanding, especially in patients with lower health literacy,use of infographics during routine medical encounters—especially in patients with low health literacy, andinvolving the patient in addressing adherence issues .54 In addition, clinician training interventions to explore and address adherence issues in IBD should be a research priority. In such training, nonadherence should be regarded as a dynamic and relational factor that can happen from time to time for various reasons, for any patient.56 Beyond adherence alone, clinician training interventions with a broad focus on providing appropriate information and shared decision-making should be on the IBD research and practice agenda. Further, the use of the teach-back technique, a technique for verifying patients’ understanding of their health information,57 and information provision support interventions such as infographics will likely be helpful in IBD. One reason why such approaches assist is because limited health literacy is associated with poorer outcomes in IBD58 and complex medical concepts could create a communication barrier between patients and IBD clinicians and hinder patient involvement.Targeted research on the way doctors and patients discuss costs and benefits of medications is likely to be useful in IBD where medication take-up and adherence is an important clinical focus, along with lifestyle measures, over many phases of life.47 Clinician behaviors known to provide support for the patient include stating one’s continued support and care, and acknowledging and addressing the patient’s concerns.47 In addition, empathy has been shown to be associated with positive clinical outcomes in diabetes.48 Findings from these studies are relevant to IBD where disease burden is high for patients and patients can be repeatedly confronted with bad news. Basic psychological support training for IBD clinicians including training on supportive communication strategies and empathic communication, therefore, should be a priority.Standard mental health screening questionnaires that provide a quick snapshot of the patient’s mental health with the aim of identifying patients who need specialist psychological support are important and efficient tools for providing psychological support to patients with chronic diseases such as IBD. However, it is highly likely that patients who are assessed as not needing specialist psychological support may still benefit from some sort of psychological support from their treating clinician. For example, evidence shows that using supportive communication when delivering bad news lowers heart rate variability.60 The role of telehealth and remote IBD care in maintaining equitable and continuous care has been further reinforced by the COVID-19 pandemic.61–63 An important aspect of telehealth in IBD that has received little attention is clinical communication. Guidelines and telehealth communication competencies training that extends beyond technical challenges are lacking. Evidence on clinical communication in telehealth beyond IBD is also limited but has been growing since 2020 due to an acceleration in using and refining telehealth technologies during the pandemic.Research on the use of e-health technology in IBD has been accumulating over the last 10 years, motivated in particular by the growing demands on limited IBD care resources, the complex nature of IBD, and the necessity of taking a “proactive” rather than “reactive” approach to disease management.The following recommendations have been extracted from health communication research beyond IBD care. 64 This is particularly relevant to clinical communication in advicelines where patients are more likely to contact the adviceline with an urgent concern and where research has identified not adequately listening to the caller as the most common reason for malpractice claims.65 For both the clinician and the patient, telehealth is a healthcare setting with a risk of multiple distractions, thus any potential care challenges, including but not limited to concerns around medications , the patient’s expectations of the treatment plan, communication issues, administrative issues, etc. should also be discussed or at least listed for discussion at the beginning of the telehealth consultation.66 This consensus-based recommendation was initially made in the context of integrative oncology, but we believe that the recommendation to discuss care challenges at the beginning of the consultation could apply to IBD telehealth consultations with specialists, nurses, and dietitians due to a higher risk of distraction associated with telehealth consultations, in general.To respect the patient’s voice, it is recommended that patients are provided some time and space to explain the reason for their call as the first few seconds of a telehealth consultation are crucial and can influence how the consultation unfolds.67 To ensure understanding of the patient’s concerns, clinicians are advised to clarify their understanding of the patient’s concerns by providing a summary of the patient’s report and seeking their confirmation.64 It is also recommended that any potential additional concerns or questions are probed near the start of the consultation and, again, before ending.68 Clinicians are advised to address at least one patient-reported outcome in the discussion of the recommended treatment and encouraged to use an interactive approach to support patient involvement and avoid long stretches of unidirectional speech (monologue).66 While the general evidence on patient involvement that was discussed in the previous section still applies to communication in telehealth, clinicians should be mindful of limitations associated with this medium of communication including latency (voice delay), turn-taking, and overlapping talk that could potentially influence the dynamics of the consultation or create misunderstandings, especially for the patients.69In telehealth consultations, it is recommended that clinicians ask direct questions about patients’ emotional state because a patient’s distress can be difficult to interpret in the absence of physical contact.70 This is a significant issue for those with comprehension difficulties including older patients with impaired speech perception, patients with cognitive-linguistic impairment, or those from culturally and linguistically diverse backgrounds. As such, it is recommended that clinicians consider shortening the telehealth consultation session by using high-quality supplementary resources that could be shared with the patients before, during, or after the session, if appropriate66 or consider discussing complex issues in a face-to-face consultation.73 Evidence from general practice suggests that telephone consultations are most appropriate for discussing single non-complex concerns and that discussions of new acute problems over the phone are almost always followed up by an in-person consultation.73 Shortening the telehealth sessions could also reduce telehealth-associated clinician cognitive load and result in enhanced clinician empathy.74 Clinicians are also advised to avoid using overly technical language when giving patients instructions regarding their care.71 Clinicians should ensure the information and instructions they give are clear to the patient by checking the patient’s understanding.72 Furthermore, allowing the patient to record the session and providing a written summary of the recommendations are among strategies recommended in the literature for effective communication in a telehealth setting.66Research has revealed a significant memory deficit for health information provided through telehealth compared with face-to-face consultations.In this narrative review, we reviewed the existing evidence on communication strategies that are linked to improved health outcomes in chronic disease management and have particular relevance for managing IBD. In addition, we synthesized the available recommendations for communication using telehealth technologies that are relevant to IBD. It also is important for future IBD research to engage with this evidence base in developing consumer communication support interventions as well as interventions for clinicians. In line with the synthesized evidence, the following communication support interventions could be beneficial in IBD management in terms of promoting patient involvement and improving disease outcomes:the question prompt list which is a structured list of potential questions that a patient may want to ask their healthcare provider,consumer interventions such as patient decision aids and online micro-lessons that focus on IBD, symptoms and their effect on daily life, medications, their usage and side-effects, healthcare maintenance, investigations and interpretation of results, as well as the relationship with the IBD clinical team including the patient’s role in IBD management and decision-making,clinician training interventions with a focus on information provision, shared decision-making exploring and respecting the patient’s voice and their values, and empathic communication,clinician training interventions for exploring and addressing adherence issues in IBD, andinformation provision interventions such as infographics to enhance patient understanding of complex medical concepts especially in patients with limited health literacy.76First, however, further research into these kinds of interventions and their implementation is needed. To take just one example, good infographic design relies on complex relations between verbal and graphic literacy among consumers and the judicious selection of clinical details and clinical metaphors. Although good infographics have the potential to make explanations clearer, poor design can in fact reduce accuracy of patient understanding.This narrative review of evidence from the generalist healthcare communication research is useful for thinking through what is most needed in IBD and how to address these needs but more IBD-specific research is needed. Communication research based on observation of how people with IBD talk with their doctors, nurses, pharmacists, dietitians, and other clinicians is a crucial but largely missing link.