Code Templates Research Articles

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Abstract Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible “pain genes”. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics, protein-protein interaction network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a “pain score” per gene. The top ranked genes were validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of “pain” genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research.

Leadership Lingo: Developing a Shared Language of Leadership Behaviors to Enrich Debriefing Conversations.

Leadership in medical emergencies is variable and frequently suboptimal, contributing to poor patient care and outcomes. Simulation training can improve leadership in both simulated practice and real clinical emergencies. Thoughtful debriefing is essential. However, unclear language around leadership limits facilitators' capacity for transformative reflective discussion. Internal medicine trainees participated in simulated medical emergency scenarios. Video recordings of consenting participants were analyzed using template analysis. A priori codes from existing literature formed an initial coding template. This was modified with inductive codes from the observed behaviors to develop a taxonomy of leadership behaviors in simulated medical emergencies. The taxonomy was then transformed into an infographic, to be used as a leadership debriefing tool. The taxonomy of leadership behaviors consisted of the following 4 themes: Structuring, Decision making, Supporting, and Communicating. Structuring behaviors shaped the team, ensuring that the right people were in the right place at the right time. Decision-making behaviors steered the team, setting a direction and course of action. Communicating behaviors connected the team, sharing valuable information. Supporting behaviors nurtured the team, guiding team members to perform at their optimum level. Debriefing-as-imagined is not always debriefing-as-done. A shared language of leadership can connect educators and learners, advancing critical debriefing conversations and enabling facilitators to drive meaningful reflective discussion. The use of infographics in simulation offers an opportunity to support educators in facilitating complex debriefing conversations.

EMPOWERING END-OF-LIFE CONVERSATIONS: The Role of Specialized Nursing Teams in Facilitating Code Status Changes at Discharge.

Objectives: Hospitalized patients may require goals of care (GOC) or Advance Health Care Planning (ACP), which can be time-consuming and emotionally tolling for providers. A nursing team specializing in code status (CODE), GOC, and ACP was developed to provide meaningful support for patients and families and decrease provider burden. Interest in CODE, GOC, ACP, and effectiveness of a nursing team to lead these conversations prompted this study. Methods: A collaborative nursing team was trained to address CODE, GOC, and ACP with patients demonstrating illness or geriatric syndrome. This team conducted 3 visits per patient on average during hospitalization using structured CODE templates to establish longer term goals and document what matters in the healthcare journey. Comprehensive narratives for ACP and GOC were included in charting, syncing the medical team, nursing, patient, and family. Consults were tracked over nine months with data reviewed retrospectively from medical charts. Descriptive analyses of cohort demographics, CODE and outcomes were completed. Results: The study group comprised 3342 patients between October 2022 and June 2023. Patients ranged in age from 18-106 years, with majority (88%) age 65 years and older. Mean length of stay (LOS) was 6.8 days with CODE documented for 91% upon admission. Of the 3166 older adults with known CODE on admission, 946 (30%) changed CODE by discharge, of which 95% were de-escalated. 83% of older patients arriving with limited CODE maintained limitations at discharge, with a small portion converting to comfort (16%). Conclusion: Employing a focused nursing team to conduct CODE, GOC, and ACP conversations may be an effective use of time and resources and result in de-escalation of resuscitation orders for patients demonstrating illness or geriatric syndrome.

Investigating the effects of congruence between within-person associations: A comparison of two extensions of response surface analysis.

Response surface analysis (RSA) allows researchers to study whether the degree of congruence between two predictor variables is related to a potential psychological outcome. Here, we adapt RSA to the case in which the two predictor variables whose congruence is of interest refer to individual differences in within-person associations (WPAs) between variables that fluctuate over time. For example, a WPA-congruence hypothesis in research on romantic relationships could posit that partners are happier when they have similar social reactivities-that is, when they have similarly strong WPAs between the quantity of their social interactions and their momentary well-being. One method for testing a WPA-congruence hypothesis is a two-step approach in which the individuals' WPAs are first estimated as random slopes in respective multilevel models, and then these estimates are used as predictors in a regular RSA. As an alternative, we suggest combining RSA with multilevel structural equation modeling (MSEM) by specifying the WPAs as random slopes in the structural equation and using their latent second-order terms to predict the outcome on Level 2. We introduce both approaches and provide and explain their corresponding computer code templates. We also compared the two approaches with a simulation study and found that the MSEM model-despite its complexities (e.g., nonlinear functions of latent slopes)-has advantages over the two-step approach. We conclude that the MSEM approach should be used in practice. We demonstrate its application using data from a daily diary study and offer guidance for important decisions (e.g., about standardization). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reclaiming identities: exploring the influence of simulation on refugee doctors’ workforce integration

BackgroundHealthcare professionals are a precious resource, however, if they fail to integrate into the workforce, they are likely to relocate. Refugee doctors face workforce integration challenges including differences in language and culture, educational background, reduced confidence, and sense of identity. It has been proposed that simulation programmes may have the power to influence workforce integration. This study aimed to explore how an immersive simulation programme influenced workforce integration for refugee doctors joining a new healthcare system.MethodsDoctors were referred to a six-day immersive simulation programme by a refugee doctor charity. Following the simulation programme, they were invited to participate in the study. Semi-structured interviews, based on the ‘pillars’ conceptual model of workforce integration, were undertaken. Data were analysed using template analysis, with the workforce integration conceptual model forming the initial coding template. Themes and sub-themes were modified according to the data, and new codes were constructed. Data were presented as an elaborated pillars model, exploring the relationship between simulation and workforce integration.ResultsFourteen doctors participated. The ‘learning pillar’ comprised communication, culture, clinical skills and knowledge, healthcare systems and assessment, with a new sub-theme of role expectations. The ‘connecting pillar’ comprised bonds and bridges, which were strengthened by the simulation programme. The ‘being pillar’ encompassed the reclaiming of the doctor’s identity and the formation of a new social identity as an international medical graduate. Simulation opportunities sometimes provided ‘building blocks’ for the pillars, but at other times opportunities were missed. There was also an example of the simulation programme threatening one of the integration pillars.ConclusionsOpportunities provided within simulation programmes may help refugee doctors form social connections and aid learning in a variety of domains. Learning, social connections, and skills application in simulation may help doctors to reclaim their professional identities, and forge new identities as international medical graduates. Fundamentally, simulation experiences allow newcomers to understand what is expected of them. These processes are key to successful workforce integration. The simulation community should be curious about the potential of simulation experiences to influence integration, whilst also considering the possibility of unintentional ‘othering’ between faculty and participants.

DeepMIP-Eocene-p1: multi-model dataset and interactive web application for Eocene climate research

Paleoclimate model simulations provide reference data to help interpret the geological record and offer a unique opportunity to evaluate the performance of current models under diverse boundary conditions. Here, we present a dataset of 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ~ 50 million years ago) and corresponding preindustrial reference experiments. To streamline the use of the data, we apply standardised naming conventions and quality checks across eight modelling groups that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Gridded model fields can be downloaded from an online repository or accessed through a new web application that provides interactive data exploration. Local model data can be extracted in CSV format or visualised online for streamlined model-data comparisons. Additionally, processing and visualisation code templates may serve as a starting point for advanced analysis. The dataset and online platform aim to simplify accessing and handling complex data, prevent common processing issues, and facilitate the sharing of climate model data across disciplines.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people’s intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual’s social reactivity – their emotional response to social situations – can be represented as the association between repeated measurements of the individual’s social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes – a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs – such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

AG-SpTRSV: An Automatic Framework to Optimize Sparse Triangular Solve on GPUs

Sparse Triangular Solve (SpTRSV) has long been an essential kernel in the field of scientific computing. Due to its low computational intensity and internal data dependencies, SpTRSV is hard to implement and optimize on GPUs. Based on our experimental observations, existing implementations on GPUs fail to achieve the optimal performance due to their sub-optimal parallelism setups and code implementations, and lack of consideration of the irregular data distribution. Moreover, their algorithm design lacks the adaptability to different input matrices, which may involve substantial manual efforts of algorithm redesigning and parameter tuning for performance consistency. In this work, we propose AG-SpTRSV, an automatic framework to optimize SpTRSV on GPUs, which provides high performance on various matrices while eliminating the costs of manual design. AG-SpTRSV abstracts the procedures of optimizing an SpTRSV kernel as a scheme and constructs a comprehensive optimization space based on it. By defining a unified code template and preparing code variants, AG-SpTRSV enables fine-grained dynamic parallelism and adaptive code optimizations to handle various tasks. Through computation graph transformation and multi-hierarchy heuristic scheduling, AG-SpTRSV generates schemes for task partitioning and mapping, which effectively address the issues of irregular data distribution and internal data dependencies. AG-SpTRSV searches for the best scheme to optimize the target kernel for the specific matrix. A learned lightweight performance model is also introduced to reduce search costs and provide an efficient end-to-end solution. Experimental results with SuiteSparse Matrix Collection on NVIDIA Tesla A100 and RTX 3080 Ti show that AG-SpTRSV outperforms state-of-the-art implementations with geometric average speedups of 2.12x ∼ 3.99x. With the performance model enabled, AG-SpTRSV can provide an efficient end-to-end solution, with preprocessing times ranging from 3.4 to 245 times of the execution time.

Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges.

Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics.

LLM4VV: Developing LLM-driven testsuite for compiler validation

Large language models (LLMs) are a new and powerful tool for a wide span of applications involving natural language and demonstrate impressive code generation abilities. The goal of this work is to automatically generate tests and use these tests to validate and verify compiler implementations of a directive-based parallel programming paradigm, OpenACC. To do so, in this paper, we explore the capabilities of state-of-the-art LLMs, including open-source LLMs - Meta’s Codellama, Phind’s fine-tuned version of Codellama, Deepseek’s Deepseek Coder and closed-source LLMs - OpenAI’s GPT-3.5-Turbo and GPT-4-Turbo. We further fine-tune the open-source LLMs and GPT-3.5-Turbo using our own testsuite dataset along with using the OpenACC specification. We also explored these LLMs using various prompt engineering techniques that include code template, template with retrieval-augmented generation (RAG), one-shot example, one-shot with RAG, expressive prompt with code template and RAG. This paper highlights our findings from over 5000 tests generated via all the above mentioned methods. Our contributions include: (a) exploring the capabilities of the latest and relevant LLMs for code generation, (b) investigating fine-tuning and prompt methods, and (c) analyzing the outcome of LLMs generated tests including manually analysis of representative set of tests. We found the LLM Deepseek-Coder-33b-Instruct produced the most passing tests followed by GPT-4-Turbo.

A Python Project Template for Healthy Scientific Software

The creation of “healthy” scientific software is vital for its successful long-term adoption in scientific research. Here healthy code is defined to mean software that is usable, maintainable, and proffers consistently reproducible results. Incorporating tooling and practices to achieve these goals often leads to short-term, yet significant, overhead for new projects. We introduce the LINCC Frameworks Python Project Template, a configurable code template designed for scientific software projects that greatly simplifies adopting best practices by automating the setup and configuration of important tools locally and via a suite of GitHub workflows. Notably, the template does not include any application-specific code, thereby enabling users to focus on their scientific code rather than building or maintaining code infrastructure.

The patient experience of a postpartum readmission for hypertension: a qualitative study

BackgroundHypertensive disorders of pregnancy (HDP) are the most common cause of postpartum readmission. Prior research led to clinical guidelines for postpartum management; however, the patient experience is often missing from this work. The objective of this study is to understand the perspective of patients readmitted for postpartum hypertension.MethodsThis was a qualitative study with data generated through semi-structured interviews. Patients readmitted with postpartum HDP at an urban academic medical center from February to December 2022 were approached and consented for an interview. The same researcher conducted all interviews and patient recruitment continued until thematic saturation was reached (n = 9). Two coders coded all interviews using Nvivo software with both deductive and inductive coding processes. Discrepancies were discussed and resolved with consensus among the two coders. Themes were identified through an initial a priori template of codes which were expanded upon using grounded theory, and researchers were reflexive in their thematic generation.ResultsSix themes were generated: every pregnancy is different, symptoms of preeclampsia are easily dismissed or minimized by both patient and providers, miscommunication regarding medical changes can increase the risk of readmissions, postpartum care coordination and readmission logistics at our hospital could be improved to facilitate caring for a newborn, postpartum care is often considered separately from the rest of pregnancy, and patient well-being improved when conversations acknowledged the struggles of readmission.ConclusionsThis qualitative research study revealed patient-identified gaps in care that may have led to readmission for hypertensive disorders of pregnancy. The specific recommendations that emerge from these themes include addressing barriers to blood pressure management prior to discharge, improving postpartum discharge follow-up, providing newborn care coordination, and improving counseling on the risk of postpartum preeclampsia during discharge. Incorporating these patient perspectives in hospital discharge policy can be helpful in creating patient-centered systems of care and may help reduce rates of readmission.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Modelling individual variability in habitat selection and movement using integrated step‐selection analysis

Abstract Integrated step‐selection analysis (ISSA) is frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSA have been developed, making it possible to quantify variability among animals in their space‐use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily available software or examples demonstrating methods for estimating movement parameters in ISSA with random effects. We demonstrated methods for characterizing among‐individual variability in both movement and habitat‐selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fish's current location and the nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat‐selection parameters using random effects. The simulation example highlights the benefits of a mixed‐effects specification, namely, we can increase precision when estimating individual‐specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among‐individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near ± π but were more dispersed when fish were far away from edge habitat. We provide code templates and functions for quantifying variability in movement and habitat‐selection parameters when implementing ISSA with random effects. In doing so, we hope to encourage ecologists conducting ISSA to take full advantage of their ability to model among‐individual variability in both habitat‐selection and movement patterns.

Approaches and methods used to bring together Indigenous and Environmental science Knowledge in environmental research: A systematic map protocol

Abstract The bringing together of multiple knowledge sources, such as Indigenous knowledge (IK) and Environmental science Knowledge (ESK), is a topic of considerable interest and significance in environmental research. In the areas of resource management for example, the bringing together of IK and ESK datasets has raised considerable interest for its potential to increase understanding and provide insights into complex phenomena such as the effects of climate change and variability on wildlife health and distribution. The potential benefits that exist from merging these knowledge sources have been widely acknowledged. However, navigating the complex processes involved in knowledge linking continues to pose significant challenges. This systematic mapping protocol will guide the collection and analysis of literature to examine the approaches and methods used in published studies that aim to bring together Indigenous and Environmental science Knowledge in environmental research. The particular focus of this examination is placed on identification of the types of approaches and methods used to merge IK and ESK datasets at the stages of data analysis, results, and interpretation/discussion in the research process. Through a scoping exercise, a draft search string was developed based on a predetermined list of keywords. Consultation was held with a senior Indigenous scholar to advise on the keywords used and consideration for IK likely to be represented in the collected literature. The final search string will be applied to online bibliographic databases to collect studies published in peer‐reviewed journals. The final capture of the search will be screened in two stages: (1) at the level of title and abstract and (2) at full‐text. All studies included will be coded using a standardised coding template and a narrative synthesis approach will be used to identify patterns in the evidence, including knowledge gaps and clusters. Practical implication: The resulting systematic map, following the outlined procedures in this protocol and considering guidelines from the Collaboration for Environmental Evidence (CEE) and Reporting standards for Systematic Evidence Syntheses (ROSES), can serve to support and inform future research endeavours engaged in working towards the linking of IK and ESK, with practical implications for communities and policymakers.

Good Developer Experience with Platform Engineering and Devops

Abstract: Experienced developers are essential for software teams to construct and deploy applications proficiently. Platform engineering aims to grant developers self-service access to preconfigured environments and tools to accelerate development lifecycles and reduce cognitive load. This article investigates how specialized DevOps teams can establish and maintain internal developer portals (IDP) to augment the developer experience. The analysis includes the automated provisioning of GitHub repositories containing code templates for the supporting infrastructure of a developer portal for a Java application. Further elements emphasized as crucial constituents of an exceptional developer experience are expert support and the maintenance of up-to-date templates

"We always felt psychologically unstable": A qualitative study of midwives' experiences in providing maternity care during the COVID-19 pandemic in Tanzania.

We explored midwives' experiences and challenges in providing maternity care during the period of the first surge of the COVID-19 pandemic in Dodoma. Phenomenological study design was used to explore midwives' experiences and challenges in providing maternity care during the COVID-19 pandemic in Tanzania. We conducted interviews with a total of 23 midwives, using semi-structured interviews and an audio recorder. Five focus group discussions (FGDs) were conducted. To analyse the data, we used interpretive phenomenological thematic data analysis with NVivo software. To analyse templates, we followed a step-by-step process, starting with familiarizing ourselves with the data, followed by preliminary coding, organizing themes, developing an initial coding template, using the initial template, refining the template and finally applying it to the entire dataset. It was found that three themes and eight subthemes merged in the current study. The main themes were ① mental health distress, ② work environment challenges and ③ isolation of midwives by different groups. Reducing risk and protecting midwives from infectious diseases will improve the workforce, reduce the number of hospital stays, reduce the cost of hospital services, improve birth outcomes and indirectly improve family, community and national economies. Due to the system's failure to provide psychological support, protective environment and isolation rooms for confirmed or suspected cases put midwives at a higher risk of contracting the virus and had to work in a stressful environment. In addition to having the right tools, midwives must also receive emotional and psychological support in order to be at their best. The system must ensure that midwives are ready for uncertain times, such as pandemic infectious disease outbreaks, by providing protective work place environment such as personal protective equipment, psychological support and isolation room for confirmed COVID-19 cases.

Design of Home Automation System with Emergency Control

In today's society, there is a growing demand for smart home solutions that not only enhance convenience but also make a significant impact on affordability and widespread adoption. This paper describes the proposed design of a Node MCU-based Home Automation system with an integrated Wi-Fi module that enables internet-based remote control of home devices. The design utilizes a basic code template to connect with the Tasmota Web interface to configure the ESP module. The system is integrated with Alexa, offering various integration options such as routines, schedules and alarms. Furthermore, it adds a manual control feature for situations where there is no internet connection and an additional level of safety through emergency controls in the event of electronic failure. A comparison of energy usage with and without the use of the proposed system is also reported. A noticeable decrease in daily electricity consumption is observed on using the designed automation system. To ensure cost-effectiveness, locally available components are utilized in the design. Consequently, it ensures that enhanced comfort, convenience, and control over one's living environment are available to a wider audience.

Leading primary care under the weight of COVID-19: how leadership was enacted in six australian general practices during 2020.

The COVID-19 pandemic challenged health care delivery globally, providing unique challenges to primary care. Australia's primary healthcare system (primarily general practices) was integral to the response. COVID-19 tested the ability of primary health care to respond to the greater urgency and magnitude than previous pandemics. Early reflections highlighted the critical role of leaders in helping organisations negotiate the pandemic's consequences. This study explores how general practice leadership was enacted during 2020, highlighting how leadership attributes were implemented to support practice teams. We performed secondary analysis on data from a participatory prospective qualitative case study involving six general practices in Melbourne, Victoria, between April 2020 and February 2021. The initial coding template based on Miller et al.'s relationship-centred model informed a reflexive thematic approach to data re-analysis, focused on leadership. Our interpretation was informed by Crabtree et al.'s leadership model. All practices realigned clinical and organisational routines in the early months of the pandemic - hierarchical leadership styles often allowing rapid early responses. Yet power imbalances and exclusive communication channels at times left practice members feeling isolated. Positive team morale and interdisciplinary teamwork influenced practices' ability to foster emergent leaders. However, emergence of leaders generally represented an inherent 'need' for authoritative figures in the crisis, rather than deliberate fostering of leadership. This study demonstrates the importance of collaborative leadership during crises while highlighting areas for better preparedness. Promoting interdisciplinary communication and implementing formal leadership training in crisis management in the general practice setting is crucial for future pandemics.

The Rubik's cube of doing and being: Factors influencing professional identity transition to the medical registrar.

Professional identity transitions, such as the transition to medical registrar, are challenging. How minoritised identities influence transitions during medical training requires further study. This study aimed to explore the factors influencing the transition to the medical registrar in Scotland to guide support during training. Interviews exploring this transition with internal medicine trainees were audio recorded, transcribed verbatim and double-coded using template analysis. We applied an initial coding template informed by multiple and multidimensional transition theory of individual, interpersonal, systemic and macro-level factors. Using a critical theory lens, a further template analysis specifically sought to understand how trainees' social identities interacted with the various levels. Nineteen IM trainees were interviewed between January 2021 and February 2022. Influential factors reflected a parallel process of competence (doing) and identity (being) development. The interaction of social identities, such as gender (being a woman) and country of origin (being an international medical graduate), occurred across levels. This can be conceptualised as a Rubik's cube with the interplay between doing and being from an individual to a macro level with trainees' social identities interacting at all levels. The transition to the medical registrar is multifaceted; with a challenging balance between support and independence in providing opportunities to perform (doing) whilst identity develops (being). Identity transitions involve multiple Rubik's-cube-like rotations between the facets of 'doing' and 'being,' until these align. Taking heed of influential factors and the interaction of minoritised social identities could guide a trainee-centred and smoother transition.