Clinical Environment Research Articles

Senior midwives’ perspectives on the transition experience of newly graduated undergraduate midwives in China: a qualitative study

IntroductionNewly graduated (NG) midwives face various challenges during their transition from school to clinical practice, which can impact their long-term professional satisfaction and development. In China, there are currently no formal support programs for NG midwives. Senior midwives, as direct supervisors and mentors of NG midwives, hold valuable insights and recommendations that could inform the development of NG midwives training programmes in clinical practice, However, these perspectives remain largely underexplored. To address this gap, this study aims to explore senior midwives’ perspectives on the transition experiences of NG midwives and their suggestions to support better adaptation to clinical practice, contributing to improvements in midwifery training systems.MethodsSenior midwives (n = 23) from seven tertiary teaching hospitals in Guangzhou, Dongguan and Shantou participated in this study using a purposive and snowball sampling approach. Focus group interviews were conducted between February 2023 and December 2023. Data were analysed thematically using NVivo 11.ResultsThis study explored senior midwives’ perspectives and suggestions regarding the transition experiences of NG midwives, and identified four major themes: professional quality, maternal and newborn care knowledge and skills, public health care & integrative competency, and career development and professional identity. For professional quality, NG midwives were considered to require a deeper understanding of natural birth, the ability to build trusting relationships with women, provide emotional support, and establish appropriate emotional boundaries. In terms of improving NG midwives’ knowledge and skills in maternal and newborn care, senior midwives emphasized the need to expand NG midwives’ professional knowledge and effectively integrate theoretical knowledge with practice through diverse learning approaches. In the area of public health care and integrative competency, NG midwives should be equipped to prevent and manage occupational exposure and possess cooperation ability. Senior midwives suggested that rational allocation of human resources could help reduce exposure risks, facilitate teamwork, and support the integration of NG midwives into the clinical environment. In addition, senior midwives generally expressed concern and expectations for the career development of NG midwives, indicating that clear career plan during the transition period could enhance their sense of satisfaction and professional belonging, thus promoting the formation of their professional identity and retention.ConclusionThe transition period is a critical phase in the career of NG midwives. Currently, NG undergraduate midwives are not fully equipped with the competency and supportive environment needed for a smooth transition. Recognizing the significance of this transition is essential for training and retaining qualified practitioners. The perspectives and suggestions of senior midwives provide valuable insights into this period, complementing existing research on the transition of NG midwives. It is imperative to refine undergraduate midwifery education systems and create stable professional environments to ensure the sustained and robust development of a qualified midwifery workforce.

A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm

BackgroundDespite significant advances in AI-driven medical diagnostics, the integration of large language models (LLMs) into psychiatric practice presents unique challenges. While LLMs demonstrate high accuracy in controlled settings, their performance in collaborative clinical environments remains unclear. This study examined whether LLMs exhibit conformity behavior under social pressure across different diagnostic certainty levels, with a particular focus on psychiatric assessment.MethodsUsing an adapted Asch paradigm, we conducted a controlled trial examining GPT-4o’s performance across three domains representing increasing levels of diagnostic uncertainty: circle similarity judgments (high certainty), brain tumor identification (intermediate certainty), and psychiatric assessment using children’s drawings (high uncertainty). The study employed a 3 × 3 factorial design with three pressure conditions: no pressure, full pressure (five consecutive incorrect peer responses), and partial pressure (mixed correct and incorrect peer responses). We conducted 10 trials per condition combination (90 total observations), using standardized prompts and multiple-choice responses. The binomial test and chi-square analyses assessed performance differences across conditions.ResultsUnder no pressure, GPT-4o achieved 100% accuracy across all domains. Under full pressure, accuracy declined systematically with increasing diagnostic uncertainty: 50% in circle recognition, 40% in tumor identification, and 0% in psychiatric assessment. Partial pressure showed a similar pattern, with maintained accuracy in basic tasks (80% in circle recognition, 100% in tumor identification) but complete failure in psychiatric assessment (0%). All differences between no pressure and pressure conditions were statistically significant (P <.05), with the most severe effects observed in psychiatric assessment (χ²₁=16.20, P <.001).ConclusionsThis study reveals that LLMs exhibit conformity patterns that intensify with diagnostic uncertainty, culminating in complete performance failure in psychiatric assessment under social pressure. These findings suggest that successful implementation of AI in psychiatry requires careful consideration of social dynamics and the inherent uncertainty in psychiatric diagnosis. Future research should validate these findings across different AI systems and diagnostic tools while developing strategies to maintain AI independence in clinical settings.Trial registrationNot applicable.

Monoclonal antibody administration in an academic institution and private neurological practice: a tale of two clinics.

The emergence of monoclonal antibody (MABs) drugs since the FDA approval of lecanemab has resulted in dramatic changes in the clinical approach and management of early-stage Alzheimer's disease (AD). Challenges with MAB adoption into clinical practice may vary depending on whether the institution is an academic/integrated healthcare organization versus a private neurological practice. We have combined demographic and clinical data from a high-volume East coast private neurology practice and a West coast academic memory clinic at post-MAB adoption. Combined data of N = 165 patient showed the following demographics: mean age 72, 67% female, 92% Caucasian, average MOCA 18/30 with amyloid status confirmed by CSF in 72% of patients. Overall, ARIA rates were 8% for ARIA-E and 7% for ARIA-H, and there were no mortalities over the observation period. Three patients required immediate medical attention due to ARIA radiographic findings associated with clinical symptoms. The private practice enrolled patients with lower averagecognitive screening scores than the academic practice, but was more efficient at initiation therapy (mean # of weeks between diagnosis and treatment 97 versus 149 days). The average patient out-of-pocket cost was ($654.38) significantly less than the 20% of the annual drug cost as previously estimated. The findings from two separate clinical environments support the notion that ARIA risk associated with lecanemab is no greater than what was found in the CLARITY-AD trial and that the costs to the patient were less than predicted. This study was limited by the lack of 12month efficacy data. Additional real-world data relating to the clinical effectiveness of MAB use in clinical practice will be necessary to best determine the risk/benefit ratio of these drugs in community populations.

Drug delivery dynamics dictate evolution of bacterial antibiotic responses

Abstract Microbes inhabit natural environments that are remarkably dynamic. Therefore, microbes harbor regulated genetic mechanisms to sense shifts in conditions and induce the appropriate responses. Recent studies suggest that the initial evolution of microbes occupying new niches favors mutations in regulatory pathways. However, it is not clear how this evolution is affected by how quickly conditions change (i.e. dynamics), or which mechanisms are commonly used to implement new regulation. Here, we perform experimental evolution on continuous cultures of E. coli carrying the tetracycline resistance tet operon to identify specific mutations that adapt drug responses to different dynamic regimens of drug administration. We find that cultures evolved under gradually increasing tetracycline concentrations show no mutations in the tet operon, but instead a predominance of fine-tuning mutations increasing the affinity of an alternative efflux pump AcrB to tetracycline. When cultures are instead periodically exposed to large drug doses, all populations evolved transposon insertions in repressor TetR, resulting in loss of regulation and constitutive expression of efflux pump TetA. We use a mathematical model of the dynamics of antibiotic responses to show that sudden exposure to large drug concentrations overwhelm regulated responses, which cannot induce resistance fast enough, resulting in selection for constitutive expression of resistance. These results help explain the frequent loss of regulation of antibiotic resistance by pathogens evolved in clinical environments. Our experiment supports the notion that initial evolution in new ecological niches proceeds largely through regulatory mutations and suggests that transposon insertions are a main mechanism driving this process.

Professionals' Perceptions of the Management of Digital Competence Sharing in Healthcare and Associated Background Factors: A Cross-Sectional Study.

To describe professionals' perceptions of the management of digital competence sharing in healthcare and associated background factors. A descriptive cross-sectional study. The study used an online survey involving 227 healthcare professionals from three public and one private healthcare organisation in Finland. Data was collected using the management of digital competence sharing (MDCS) instrument and analysed using descriptive statistics, independent sample t-tests and one-way ANOVA. Based on the professionals' perceptions, the overall management of digital competence sharing was weak. They perceived the highest level of creation of a friendly and safe digital organisational atmosphere while the lowest level of provision of resources and opportunities for digital competence sharing. Background factors, including gender, age, work experience in healthcare, organisation and clinical environment, showed statistically significant differences in how professionals perceived the management of digital competence sharing. The results emphasised the need for increased managers' attention to digital competence development, prioritising and supporting digital competence sharing among healthcare professionals. The results can be utilised in healthcare management to enhance the digital competence sharing among healthcare professionals and the use of existing digital competence to benefit the work community. The importance of digital competence is increasing among healthcare professionals, but at the same time, they perceive inadequate management support in this area. This study revealed limited management of digital competence sharing in healthcare organisations, particularly among older professionals and those in inpatient and primary care settings. These results can be applied in managers' training to support and promote digital competence among healthcare professionals. The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist. There is no patient or public contribution.

Negotiating the dual role of performer and learner: Medical students’ impression management in the clinical learning environment

Background Existing literature suggests that learning during clinical placements is predominantly informal and unstructured, requiring medical students to be proactive in maximising learning opportunities. Students learn to emulate the characteristics of a doctor and navigate social structures of the clinical learning environment (CLE) through legitimate peripheral participation. The study aim was to explore how students present themselves and manage impressions in the CLE to optimise learning. Method An ethnographic approach included 120 h of observations conducted in two hospital wards hosting placements for medical students. Additionally, n = 13 students and n = 23 healthcare staff populating these clinical sites were interviewed to capture the lived experiences and perspectives around self-presentation (students), and impact of these performances (staff). Sensitising concepts from Goffman’s theory related to impression management served as priori guides in data analysis to identify prominent patterns. Results We identified five themes: (1) Students display a veneer of interest and engagement aligned to their understanding of the social norms, (2) Creating a positive first impression on healthcare staff is a preventive practice adopted by students to avoid interprofessional conflicts, (3) Atypical personal front of overseas doctors and students impacts their impression management, (4) Participatory learning with near-peers involves less impression management burden and consequent stress, and (5) Understanding social rules of the CLE takes time and slows learning. Conclusion The research reveals diverse ways in which medical students present themselves and their activities to others. Engineering convincing and desired impressions is an affective and cognitive task for students, in their dual position of actor-performers and learners. Our findings indicate that certain personal fronts punctuate learning, and we advocate for clinical workplaces to incorporate participatory learning opportunities, given their empowering benefits. Robust induction and allowing students to be authentically contributory in the CLE should ensure that diverse learners thrive in unfamiliar cultural spaces.

Early Detection of Huntington Diseases by Using Wearable Sensor

Abstract - Huntington’s Disease (HD) is a hereditary neurodegenerative condition characterized by the gradual degeneration of neurons, particularly in the cerebral cortex and basal ganglia. This degeneration results in motor dysfunction, cognitive impairment, and behavioral abnormalities. Among the earliest and most impactful symptoms are gait disturbances, including reduced stride length, irregular gait rhythm, and balance issues, which significantly impair mobility and quality of life. Traditional gait analysis requires expensive lab setups and lacks portability. Recent advancements in wearable sensor technology offer low-cost, real-time alternatives suitable for both clinical and home environments. This study presents a wearable monitoring system built on the Node MCU microcontroller, integrating a tri-axial accelerometer for gait analysis and sensors for Physiological monitoring—temperature, humidity (DHT11), heart rate, SpO2, and galvanic skin response (GSR). Data is transmitted wirelessly to the Thing Speak cloud for real-time visualization and remote analysis. Preliminary findings indicate that integrating gait and physiological data enhances the ability to monitor disease progression, support early intervention, and personalize therapeutic strategies. The system demonstrates promise as an accessible, non-invasive tool for continuous HD monitoring. Keywords-wearable sensor, gait analysis, physiological monitoring, real time monitoring

An Objective Method to Determine Nurse Staffing for an Acute Care for Elders (ACE) Hospital Unit: Discrete Event Simulation.

Many hospitals have acute care for elders (ACE) units or engage in programs to enhance care for older inpatients. However, few studies have objectively evaluated nurse staffing models to support care for older inpatients. This study applied discrete event simulation (DES) to an ACE unit to objectively evaluate registered nurse (RN) and nursing assistant (NA) staffing allocations. Research staff collected standardized, objective data related to nursing tasks and time requirements to model the ACE unit clinical care environment and evaluate varying RN and NA staffing allocations on measures of nursing workload, care quality, and care efficiency. On a 22-bed ACE unit, 85% of patients were aged 65 or older, 37% had cognitive impairment, and 89% required toileting and/or mobility assistance. Nurse care routines were interrupted frequently by unscheduled patient care requests, with an average frequency of 6.1 (±1.6) requests per hour. DES was used to simulate four different RN and NA staffing allocations. Results showed the most common staffing (four RNs and one NA) resulted in the highest nursing workload rates (89% and 88% for RNs and NAs, respectively) and the highest rate of predicted care omissions (6.2%). Additionally, RNs were predicted to help with 83% of NA care tasks related to toileting and mobility assistance. Alternative allocations of four RNs and three NAs or five RNs and two NAs resulted in more feasible workload rates, lower rates of care omissions, and less reliance on RNs for NA care tasks. DES provides an objective method to identify nurse staffing needs for an ACE hospital unit. This approach can be used to safely evaluate the potential impact of varying nurse staffing allocations. The DES model for the ACE unit is adaptable to other types of hospital units that care for older patients.

Making better clinical decisions: How doctors can recognise and reduce bias and noise in medical practice

Clinical reasoning and decision-making are essential to medical practice, where poor clinical judgement can lead to serious diagnostic errors, suboptimal use of diagnostic tests and investigations, flawed management strategies and ultimately, adverse patient outcomes. It is known that in real-world clinical environments, clinicians often rely on intuitive judgements for decision-making, due to natural proclivities for pattern-recognition and retrieval of pre-existing illness scripts, as well as out of practical necessity to be efficient in fast-paced work environments with high patient volume and cognitive load. Yet, an over-reliance on intuitive judgements can result in cognitive errors. While biases associated with heuristics-based or intuitive thought processes are often discussed in literature, there is also a lesser entity of noise that affects consistency and reliability of clinical decision-making. In this article, we highlight the importance of learning foundational principles of clinical reasoning and understanding how cognitive errors happen in medical training, and suggest a series of educational pedagogies and workplace-based interventions that could help to cultivate and optimise medical decision-making in real-world practice.

Learning from Reflection on Patient Outcomes Data: How EHR Can Support Trainees in Graduate Medical Education on Inpatient Rotations.

As healthcare evolves into interdisciplinary, complex, team-based care that often includes shiftwork and sub-specialization, patient outcomes data has become necessary for trainees to engage in reflective practice in clinical environments. However, current practices around collecting and distributing such data to trainees are not effective. Specifically, it is not clear what patient data are significant and compelling to trainees for reflective practice. The goal of our study was to characterize trainee perspectives on what data are meaningful to promote reflective activities for learning in the clinical work environment. From 2020-2021, we conducted a longitudinal cross-sectional study to assess trainee interest in clinical outcomes data. Over 14 days, pediatrics and internal medicine residents doing inpatient work at the University of California San Francisco completed surveys corresponding to recently opened patient charts. 958 surveys were completed by 41 participants (average 23 unique patient encounters per participant). Trainees expressed interest in follow-up for 32.9% of encounters (n = 315/958), most often to 'learn if something significant or unexpected happened.' Trainees most often desired follow-up patient data when they had made significant decisions or felt responsible. Trainees were interested in clinical outcomes data for a limited number of patient encounters, highlighting challenges with current strategies to promote reflective practice using clinical outcomes data. While refinement of such approaches continues through consideration of what trainees find meaningful in data, understanding motivating and demotivating factors in trainees' outcomes data-seeking behaviors will also be crucial for success in using such data for learning opportunities.

Development of Zn-Reinforced Mg Matrix Composites via High Energy Ball Milling Duration: Impact on Mechanical Properties and Biodegradability

In this study, Zn-reinforced Mg matrix composite materials were produced via powder metallurgy by exposing them to ball milling at varying mechanical milling times. Following ball milling, the powders were cold-pressed under 600 MPa to obtain green compacts. The sintering process was carried out in a tube furnace under an argon atmosphere at 500 °C for 120 min. The effects of different milling times (2 h, 4 h, and 8 h) on particle and grain size, as well as the influence of sintering temperature and time on the microstructure, were investigated through SEM analysis. Phase evolution and changes in crystal planes occurring after ball milling were revealed by XRD analysis. SEM images show that Zn particles were homogeneously distributed within the matrix after 8 h of milling. Furthermore, it can be clearly stated that the highest hardness values were obtained from the samples produced after 8 h of milling. The sample group with the highest density, least mass loss, and lowest degradation rate was obtained from materials produced from 4 h ball milled powders. The intermetallic phase formed in the powder structure after 8 h of milling tends to reduce density and corrosion properties. The findings reveal that the addition of these alloys to pure Mg clearly enhances its hardness and density, while also imparting superior corrosion resistance. These combined improvements suggest that the developed materials hold strong potential for application in biomedical and clinical environments, where both mechanical strength and corrosion resistance are critical.

Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome

ObjectiveThis study aims to assess risk factors and build a nomogram model to facilitate the early recognition of post-stroke complex regional pain syndrome (CRPS).MethodsA total of 587 stroke patients admitted to Dongguan Hospital of Guangzhou University of Traditional Chinese Medicine from September 2021 to October 2024 were initially included in this study. After exclusions, 376 patients were selected. Among these, there were 90 patients with post-stroke CRPS, while the non-stroke CRPS group consisted of 286 patients. Feature selection and optimization to generate the predictive model and nomogram were performed using LASSO regression and multivariable logistic regression analysis. We also utilized calibration plots, receiver operating characteristic (ROC) curves, decision curves (DCA), and clinical impact curves (CIC) for model validation.ResultsLASSO regression analysis and multivariate logistic regression identified gender, age, NIHSS score, cervical spondylosis, sleep disorders, fasting blood glucose (FBG), and albumin (ALB) as significant predictors. The nomogram model showcased reliable predictive effectiveness, achieving an area under the curve (AUC) of 0.858 (95% CI, 0.801–0.915). Both DCA and CIC demonstrated that the nomogram model holds substantial clinical utility.ConclusionThis study has developed a novel predictive model for post-stroke CRPS, providing a valuable tool to facilitate the early detection of high-risk patients in a clinical environment.

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models

In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and treatments can be advanced with the aid of technology. This work attempts to conduct one such investigation on dementia diagnosis based on EEG signals. The implementation is carried out under three different verticals. Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. Second, 1D Convolutional Neural Networks models are developed, and pre-processed EEG signals are fed as input. Third, stacked spectrogram images are computed from decomposed EEG signals and are fed to 2D CNN models for classification. The investigations are performed on three different benchmark datasets. Across three datasets, the class labels include cognitively normal, frontotemporal dementia, mild cognitive impairment, and Alzheimer’s. The study offers a comparative evaluation across three distinct datasets, illustrating that deep learning models, particularly 1D and 2D CNNs, consistently outperform conventional methods in recognizing subtle EEG signal patterns linked to neurodegenerative conditions. For instance, in Dataset 1, the 2D CNN achieved the highest accuracy of 91.13%, surpassing the Random Forest model’s 84.78% accuracy. Nevertheless, the investigation also points out challenges in Dataset 3, indicating the necessity for further model optimization tailored to specific datasets. Statistical tests validate the findings. This study showcases a comparative investigation of the potential of deep learning models vs. conventional classifiers in clinical environments for the early diagnosis of dementia.

AI-powered Alzheimer’s diagnosis: Integrating cognitive monitoring, IoT, and secure edge computing

This study proposes a privacy-preserving, multi-modal AI framework for the early detection of Alzheimer’s disease (AD), addressing the limitations of conventional single-modal diagnostic systems. The model fuses heterogeneous data sources, including physiological signals from wearable IoT devices, neuroimaging biomarkers extracted from T1-weighted MRI scans, and environmental context derived from smart home sensors. A hybrid architecture incorporating temporal CNN-LSTM networks, 3D ResNet models, attention layers, and graph neural networks is employed to extract and integrate cross-modal features. Federated learning with differential privacy (ε = 1.0) enables secure and decentralized training across distributed healthcare nodes, ensuring compliance with HIPAA and GDPR. Experimental validation on real-world datasets such as ADNI-4 and IoT-HOME shows a diagnostic accuracy of 97.3%, with a 12% improvement in recall over single-modality baselines. The system achieves sub-150 millisecond inference latency on resource-constrained edge devices through quantization and kernel pruning. Results demonstrate robust convergence, high interpretability via SHAP explanations, and scalability in heterogeneous clinical environments. The framework offers a technically robust, ethically aligned, and practically deployable solution for real-time, edge-enabled Alzheimer’s monitoring in both institutional and home-care settings.

Association of Moral Reasoning with Clinical Belongingness and Their Influential Factors among ICU Nurses

The intensive care unit (ICU) is a challenging and stressful environment where nurses encounter difficult ethical decisions daily. Therefore, this study aimed to determine the correlation between moral reasoning and clinical belongingness among Iranian ICU nurses. This cross-sectional, descriptive-analytical study was conducted on 126 nurses working in the adult ICUs of hospitals in Yasuj, Iran in 2023 using census sampling. Questionnaires measuring clinical belongingness and moral reasoning were used to collect data. Data were analyzed using independent samples t-test, ANOVA, regression, and Pearson’s correlation coefficient via SPSS-26 software. The findings revealed that the mean scores for clinical belongingness and moral reasoning among the nurses were 109.68 ± 13.75 and 36.07± 5.50, respectively, indicating good clinical belongingness and moderate moral reasoning. Moreover, a statistically significant relationship was identified between the place of service and clinical belongingness (p = 0.02), while no significant relationship was found between clinical belongingness and moral reasoning (p&gt;0.05). Although no significant relationship was established between clinical belongingness and moral reasoning, certain demographic characteristics showed a significant predictive relationship with nurses' clinical belongingness. Accordingly, it is recommended that nursing officials and managers utilize these findings to improve moral reasoning and the sense of belonging to the clinical environment among nurses.

You won’t learn until you want to: medical students’ experiences of the educational nature of the clinical environment

BackgroundAcquiring basic skills and achieving professional competence depends on the quality and quantity of training in the clinical environment. Any defects or inadequacies in the education process will impact the quality and quantity of healthcare services, and ultimately, the health of individuals and society. Given the importance of this issue, the aim of this study is to elucidate the experiences of medical students regarding the educational nature of the clinical environment.MethodsThis qualitative study employed a conventional content analysis approach and was conducted in 2024. Data were collected through semi-structured interviews with open-ended questions regarding the experience of the “educational nature of the clinical environment.” Participants were from various fields of medical sciences and were recruited using purposive sampling. Data saturation was achieved with 22 participants. Interviews were transcribed verbatim and analyzed according to the method described by Graneheim and Lundman. All ethical considerations for human research, including informed consent and confidentiality, were observed.ResultsFrom the data analysis, four main categories and eight subcategories emerged: (a) ultimately, you are alone (passion for learning, student under pressure), (b) Instructor under the student’s microscope (instructor as a refuge for students, planning for teaching), (c) Half and incomplete clinical environment (lack of resources, unequal learning opportunities), (d) Communication, key to learning in practice (better communication: more learning, accompanying the patient in learning).ConclusionsThe findings of this qualitative study provide significant insights into medical students’ experiences in the clinical setting, highlighting the need for a transformative approach to clinical education policies and practices. Students’ narratives emphasize the importance of creating an environment that encourages autonomy and active learning and addresses the pressures and challenges they face, such as inadequate resources, high stress levels, and unequal learning opportunities. To improve the educational experience, clinical education policies should prioritize equitable access to learning resources, promote a culture of respect, and collaboration among all healthcare professionals. By addressing these critical areas, clinical education can better prepare future healthcare professionals to navigate the complexities of patient care.

An Open-Source Wearable System for Real-Time Human Biomechanical Analysis

The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system’s technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscore the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods.

Dealing with the extraordinary: how a community of practice supports resident training during the COVID-19 pandemic and beyond

BackgroundThe practice of medicine occurs within a community of practice (CoP), where learning is shaped through shared purpose, the interaction of members, and the development of collective knowledge and skills. Through experiential learning, medical trainees become healthcare professionals within this community. The COVID-19 pandemic disrupted traditional training models, requiring residents to adapt under extraordinary circumstances. This study applies a CoP lens to explore how residency program directors (PD) perceived residents’ learning, competence development, and adaptation during the pandemic.MethodsA qualitative analysis was conducted on PD’s written responses to a COVID-19 survey administered by the Accreditation Council for Graduate Medical Education-International from July 1 to September 30, 2020. De-identified narrative reflections on the pandemic’s impact on clinical learning environments and training experiences were analyzed using thematic content analysis.ResultsResponses from 138 PDs in six countries were analyzed. Three themes emerged, highlighting the social and dynamic nature of experiential learning in a CoP: (1) capability building, where residents adapted by integrating and applying knowledge, skills, and attitudes as modeled by faculty and peers; (2) fostering resilience in the face of uncertainty through altruism, volunteerism, strong support networks, and collective moral resilience; and (3) collaborative working and communicating across interdisciplinary and interprofessional teams, facilitated by a flattening of traditional hierarchies.ConclusionsSupport from senior physicians and a shared sense of purpose facilitated residents’ learning in a rapidly changing, high stakes environment. This accelerated experiential learning occurred through engagement within a CoP. Our findings illustrate how the CoP framework can help residency programs foster adaptive learning and resilience during future large-scale disruptions to medical training.

Decision-Making in the Backcountry and Its Clinical Applications in Medical Education-a Pilot Experiential Learning Workshop.

Decision-Making in the Backcountry and Its Clinical Applications in Medical Education-a Pilot Experiential Learning Workshop.

The Potential Therapeutic Advantages of Bacteriophage Targeting MDR Pseudomonas aeruginosa using the Infection Model of Gallaria mellonella

Aim: The growing problem of antibiotic-resistant bacteria in clinical environ-ments has driven increased interest in bacteriophage therapy, where viruses specifically target and destroy harmful bacteria. This research explores the therapeutic potential of bac-teriophages isolated to combat Multi-drug-resistant Pseudomonas aeruginosa, using Gal-leria mellonella larvae as an experimental model. In vitro susceptibility to multiple antibi-otics was assessed using the double agar overlay technique. Additionally, the survival rate of the larvae was analyzed to determine the phages' capacity to combat bacterial infection in G. mellonella. Background: Resistance to commonly used antibiotics has been steadily increasing over the past few decades, and it has been observed to vary depending on the alternative method employed, such as Bacteriophage Therapy. Objective: Bacteriophage therapy was employed to treat moth wax larvae that were infected with Multidrug-Resistant Pseudomonas aeruginosa. Materials and Methods: The study focused on the Pseudomonas aeruginosa strain PP31, a Multidrug-Resistant Gram Negative bacterium that was obtained from biomedical waste at ICF Hospital in Tamil Nadu, India. Galleria mellonella larvae infected with this multidrug-resistant strain of Pseudomonas were employed for treatment using bacteriophage. Results: It was found that a single phage might infect a particular strain of bacteria in the host. It was demonstrated that MDR Pseudomonas aeruginosa infected larvae must be treated with a single specific phage dose (20μL, 104 PFU/mL) at 6 – hour intervals in order to achieve a 95% survival rate for In vivo research. By counting the number of germs in the larvae, the results were confirmed. Conclusion: Our research shows that although phages were shown to be highly contagious in vitro, specific phage dosages were required for effective treatment in living animals.