Training Module Research Articles

Screening and early intervention for substance use during pregnancy: A retrospective case note review of antenatal care records.

Screening for substance use during pregnancy is critical for enhancing maternal health and perinatal outcomes. However, disparities persist in screening and intervention rates within maternity services. This retrospective case note review explored contemporaneous practices around screening and interventions for substance use among pregnant women during routine antenatal care. A random sample of 100 sets of maternity records were reviewed. Eligible cases included any woman attending initial pregnancy assessments at one of two South Australian metropolitan Hospital-based antenatal clinics, from July 2019-September 2020. Screening rates for past and current alcohol, tobacco and other substance use were identified and compared with data from a subset of a nationally representative survey. Intervention details and referral pathways were also assessed. The final sample of eligible cases (n = 93) demonstrated prioritisation of screening for current use, over past use, across all substances (p < 0.001). Screening was most likely for tobacco and least likely for e-cigarettes (p < 0.001). Significant underreporting of past use compared with the benchmark was identified for all substances (except tobacco, p = 0.224). Interventions typically involved written resources, which were usually declined by clients. Despite longstanding recommendations, screening and intervention practices for substance use appear inconsistent. With the recent emergence of vaping, no evidence of updated approaches to identifying e-cigarette consumption in pregnant women was found. Several opportunities for enhancing routine screening and intervention practices within antenatal clinics were identified, and will inform the development of policy directives, targeted training modules, and other resources for health professionals working in these services.

Closing the Gaps in Hepatitis C Knowledge Among Internal Medicine Residents in the United States

BackgroundThe introduction of direct-acting antivirals (DAA) has revolutionized hepatitis C virus (HCV) treatment but has not translated into an appreciable decline in HCV prevalence, which is estimated to be 2.4 million in the United States. Efforts are thought to be limited by the lack of experience among nonspecialist providers in managing HCV. However, there have been no comprehensive surveys assessing HCV knowledge among medical trainees to determine if trends have shifted since the discovery of DAAs. MethodsWe performed a retrospective observational study of internal medicine (IM) residents in the United States (n = 1763) who completed the Physician Education and Assessment Center HCV learning module between 2021 and 2022. Participant pre- and post-test performance was compared with further stratified analysis by training year, geography, training program type, and local HCV prevalence using ANOVA and Chi-squared tests of proportions, respectively. ResultsIM residents universally lacked baseline HCV knowledge (average score ± standard deviation, 43% ± 19%); less than 50% of participants answered correctly in the majority of tested domains. There were no consistent trends in performance regardless of resident characteristic used to stratify the participants. Knowledge gaps improved after completing an online educational training module (P < .001). ConclusionsHCV knowledge remains limited among IM residents despite expansion of treatment options. Addressing these gaps during clinical training may substantially increase the availability of HCV treatment in the community, and online modules may be one means by which to integrate these efforts into medical training.

AI-KODA score application for cleanliness assessment in video capsule endoscopy frames

Background Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-CanaDA (KODA) scoring system by developing an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score, as well as to determine the inter-rater and intra-rater reliability of the KODA score among three readers for prospective AI applications, and check the efficacy of the application. Method From the 28 patient capsule videos considered, 1539 sequential frames were selected at five-minute intervals, and 634 random frames were selected at random intervals during small bowel transit. The frames were processed and shifted to AI-KODA. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated 2173 frames in duplicate four weeks apart after completing the training module on AI-KODA. The scores were saved automatically in real time. Reliability was assessed for each video using estimate of intra-class correlation coefficients (ICCs). Then, the AI dataset was developed using the frames and their respective scores, and it was subjected to automatic classification of the scores via the random forest and the k-nearest neighbors classifiers. Results For sequential frames, ICCs for inter-rater variability were ‘excellent’ to ‘good’ among the three readers, while ICCs for intra-rater variability were ‘good’ to ‘moderate’. For random frames, ICCs for inter-rater and intra-rater variability were ‘excellent’ among the three readers. The overall accuracy achieved was up to 61% for the random forest classifier and 62.38% for the k-nearest neighbors classifier. Conclusions AI-KODA automates the process of scoring VCE frames based on the existing KODA score. It saves time in cleanliness assessment and is user-friendly for research and clinical use. Comprehensive benchmarking of the AI dataset is in process.

High Electron Mobility in Si-Doped Two-Dimensional β-Ga2O3 Tuned Using Biaxial Strain.

Two-dimensional (2D) semiconductors have attracted much attention regarding their use in flexible electronic and optoelectronic devices, but the inherent poor electron mobility in conventional 2D materials severely restricts their applications. Using first-principles calculations in conjunction with Boltzmann transport theory, we systematically investigated the Si-doped 2D β-Ga2O3 structure mediated by biaxial strain, where the structural stabilities were determined by formation energy, phonon spectrum, and ab initio molecular dynamic simulation. Initially, the band gap values of Si-doped 2D β-Ga2O3 increased slightly, followed by a rapid decrease from 2.46 eV to 1.38 eV accompanied by strain modulations from -8% compressive to +8% tensile, which can be ascribed to the bigger energy elevation of the σ* anti-bonding in the conduction band minimum than that of the π bonding in the valence band maximum. Additionally, band structure calculations resolved a direct-to-indirect transition under the tensile strains. Furthermore, a significantly high electron mobility up to 4911.18 cm2 V-1 s-1 was discovered in Si-doped 2D β-Ga2O3 as the biaxial tensile strain approached 8%, which originated mainly from the decreased quantum confinement effect on the surface. The electrical conductivity was elevated with the increase in tensile strain and the enhancement of temperature from 300 K to 800 K. Our studies demonstrate the tunable electron mobilities and band structures of Si-doped 2D β-Ga2O3 using biaxial strain and shed light on its great potential in nanoscale electronics.

Evaluation of efficacy of a training module regarding biohazards associated with materials used in prosthodontics for dental students – A cross-sectional study

Objectives: This study aimed to evaluate the efficacy of a training module for dental students on biohazards associated with dental materials. Material and Methods: Single group pre- and post-test design was used. One hundred undergraduate dental students were enrolled. Ninety-two students responded to the questionnaire. Results: Post-test score improved significantly after the session. Conclusion: There was an improvement in the knowledge regarding various aspects of biohazards as a result of this module.

Simulation training in mammography with AI-generated images: a multireader study.

The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training. We developed a generative adversarial network (GAN) that was capable of generating mammography images with varying characteristics, such as size and density, and created a tool with which a user could control these characteristics. The tool allowed the user (a radiology resident) to realistically insert cancers within different regions of the mammogram. We then provided this tool to residents in training. Residents were randomized into a practice group and a non-practice group, and the difference in performance before and after practice with such a tool (in comparison to no intervention in the non-practice group) was assessed. Fifty residents participated in the study, 27 underwent simulation training, and 23 did not. There was a significant improvement in the sensitivity (7.43 percent, significant at p-value = 0.03), negative predictive value (5.05 percent, significant at p-value = 0.008) and accuracy (6.49 percent, significant at p-value = 0.01) among residents in the detection of cancer on mammograms after simulation training. Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training. Using generative artificial intelligence, simulation training modules can be developed that can help residents in training by providing them with a visual impression of a variety of different cases. Generative networks can produce diagnostic imaging with specific characteristics, potentially useful for training residents. Training with generating images improved residents' mammographic diagnostic abilities. Development of a game-like interface that exploits these networks can result in improvement in performance over a short training period.

Controller design and remote piloting training module development for unmanned cropped delta reflex wing configuration

In this study, a cost-effective high-fidelity six-degree of freedom module augmented with an onboard controller is developed. The unmanned aerial vehicle (UAV) under consideration is a wing-alone configuration with a cropped delta planform and reflex airfoil cross-section designed to perform manoeuvres at high angles of attack. The six-degree of freedom dynamics for the aforementioned configuration is developed based on Newtonian mechanics incorporating linear and nonlinear aerodynamic models to expand flight simulations in corresponding regimes. Aerodynamic stability and control parameters for the simulations are obtained from full-scale wind tunnel measurements and flight tests. It is observed that the linear aerodynamic model is valid in angle of attack (α) domain −5°≤α≤10°, followed by nonlinearity up to α≈21°, post which lift coefficient remains almost constant with angle of attack till 45°. The simulation model is validated with the corresponding flight data obtained from flight tests. A user-friendly graphical interface with real-time animation of UAV has been developed for the aforementioned configuration by integrating MATLAB and Flightgear environment with real-time control input from the hardware to enable the pilot for visual and tactile feedback, respectively. A controller based on total energy control is designed for velocity and altitude hold modes, while an attitude hold controller is designed for pitch and roll angle tracking to enable controller-in-the-loop remote piloting simulations. In order to enhance practical applicability, the designed module has been tested under various simulated wind conditions. It is noticed from the results that the steady-state error in attitude remains below 2% with a maximum overshoot of 5° from reference. The total energy controller achieves a zero steady-state error with a maximum overshoot of 2 m/s for velocity and 6 m for altitude, respectively. The developed module enhances remote piloting training, flight response assessment, and control algorithm testing for cropped delta planform UAVs.

Empowering nursing students through inclusivity training for LGBTQIA+ patients: A quasi-experimental study

BackgroundThe current state of nursing education is concerning, with nearly two-thirds of the American Association of Colleges of Nursing (AACN) and Commission of Collegiate Nursing Education (CCNE) -accredited universities reporting that their nursing graduates are ill-prepared to care for the LGBTQIA+ community (Eickhoff, 2021). This lack of training is alarming, given the ongoing reports of LGBTQIA+ healthcare discrimination and the insufficient knowledge of nursing faculty to guide future nurses in caring for this patient population (Hughes et al., 2022). AimOur study sought to address this gap by developing and assessing the effectiveness of an LGBTQIA+ inclusivity training module for Baccalaureate nursing students. DesignWe employed a quasi-experimental pretest-posttest design, evaluating nursing students' knowledge and attitudes before and after the LGBTQIA+ inclusivity training. SettingThe study was conducted in a four-year baccalaureate nursing program in the Southern United States. ParticipantsJunior-level baccalaureate nursing students. MethodsUsing a pre-test post-test method, we collected data that included the Gay Affirmative Practice Scale, an LGBTQIA+ knowledge assessment, and a computer-based LGBTQIA+ inclusivity training module. ResultsN = 30 students consented and completed all necessary study components. The findings revealed significant improvements in pre-test and post-test GAP and knowledge assessment scores following the LGBTQIA+ Inclusivity Training for baccalaureate nursing students. ConclusionsThe results of our study underscore the effectiveness of an LGBTQIA+ computer-based simulation experience for baccalaureate nursing students, marking a significant step towards improving LGBTQIA+ healthcare inclusivity in nursing education.

Comparing Learning Outcomes of Machine-Guided Virtual Reality-Based Training With Educator-Guided Training in a Metaverse Environment: Randomized Controlled Trial.

Virtual reality (VR) modules are commonly used for health care training, such as adult advanced cardiac life support (ACLS), due to immersion and engagement. The metaverse differs from current VR serious gaming by enabling shared social connections, while current VR modules focus on computer-based content without social interaction. Educators in the metaverse can foster communication and collaboration during training sessions. This study aimed to compare learning outcomes of VR-based, machine-guided training with educator-guided, VR-based training in the metaverse environment. A total of 62 volunteered students from Acibadem Mehmet Ali Aydinlar University Vocational School for Anesthesiology were randomly divided into 2 groups of 31 participants each: one group received VR-based training with machine guidance (MG), and the other received VR-based training with educator guidance (EG) in the metaverse. The members of both groups undertook VR-based basic training for ACLS. Afterward, the MG group was trained with a VR-based advanced training module, which provides training with full MG, whereas the EG group attended the VR-based, educator-guided training in the metaverse. The primary outcome of the study was determined by the exam score of the VR-based training module. Descriptive statistics defined continuous variables such as VR exam scores and time spent on machine- or educator-guided training. The correlation between training time and VR exam scores was assessed with the Spearman rank correlation, and nonnormally distributed variables were compared using the Mann-Whitney U test. Statistical significance was set at P<.05, with analyses executed by MedCalc Statistical Software (version 12.7.7). Comparing the VR test scores between the MG and EG groups revealed no statistically significant difference. The VR test scores for the EG group had a median of 86 (range 11-100). In contrast, the MG group scores had a median of 66 (range 13-100; P=.08). Regarding the correlation between the duration of machine-guided or educator-guided training and VR-based exam scores, for the MG group, =0.569 and P=.005 were obtained. For the EG group, this correlation was found to be =0.298 and P=.10. While this correlation is statistically significant for the MG group, it is not significant for the EG group. The post hoc power analysis (80%), considering the correlation between the time spent on training and exam scores, supported this finding. The results of this study suggest that a well-designed, VR-based serious gaming module with MG could provide comparable learning outcomes to VR training in the metaverse with EG for adult ACLS training. Future research with a larger sample size could explore whether social interaction with educators in a metaverse environment offers added benefits for learners. ClinicalTrials.gov NCT06288087; https://clinicaltrials.gov/study/NCT06288087.

Evidence-based early rehabilitation for children with cerebral palsy: co-development of a multifaceted knowledge translation strategy for rehabilitation professionals.

Cerebral palsy (CP) is the most common childhood physical disability. Early and evidence-based rehabilitation is essential for improving functional outcomes in children with CP. However, rehabilitation professionals face barriers to adopting evidence-based practices (EBP)s. The objective of this project is to develop a knowledge translation (KT) strategy to support CP-EBP among pediatric rehabilitation professionals. We follow an integrated KT approach by collaborating with clinician- and patient-partners. Partners engaged in co-design through team meetings and content review via email. The KT strategy comprises two components: (1) An electronic (e)-KT toolkit was created from summarized evidence extracted from randomized clinical trials on early rehabilitation for children with CP, and (2) a multifaceted online KT training program developed with guidance from a scoping review exploring effective KT strategies. The e-KT toolkit summarizes twenty-two early interventions for children with or at risk for CP aged 0-5 years. Each module features an introduction, resources, parent/family section, and clinician information, including outcomes, intervention effectiveness, and evidence level. The KT training program includes three 10-15 min video-based training modules, text summaries, quizzes, and case studies. Site champions, identified as qualified rehabilitation professionals, were onboarded to support the site implementation of the training program. A champion-training booklet and 1-hour session were designed to equip them with the necessary knowledge/resources. The tailored, multifaceted, and co-designed KT strategy aims to be implemented in pediatric rehabilitation sites to support professional's uptake of CP-EBPs. Lessons learned from its development, including the co-development process and multifaceted nature, hold potential for broader applications in rehabilitation.

Beyond bones, joints and muscles: can physiotherapists have a role in identifying and supporting domestic violence victims?

Purpose Domestic violence (DV) is a pervasive global issue affecting people across all demographics. Physiotherapists, due to their close patient contact, may be ideally positioned to identify and support victims. However, their preparedness for this role is shown to fall short of needs via a study that aimed to determine physiotherapists’ knowledge, attitudes, and experiences with DV in Estonia, Greece, Cyprus, and Spain. Methods An online questionnaire via the LimeSurvey® platform assessed the experiences of 167 physiotherapists across the four countries. The survey covered respondent demographics, training received on DV identification and referral procedures, self-reported confidence in identifying and referring DV victims, and the frequency of encountering suspected cases. Results The findings revealed a substantial gap between the legal mandates for healthcare professionals to address DV (as outlined in the Istanbul Convention and a 2024 EU Directive) and the preparedness of physiotherapists. Only 13.2% of respondents had received any DV training, and less than half felt confident making referrals to support services. However, there is an evident association between experience and awareness – physiotherapists with more years of experience, particularly those specialising in paediatrics or chronic conditions, are more likely to have encountered and reported suspected DV cases. Conclusions The study’s generalisability may be limited due to the sample size and the selection process (social media outreach and professional networks). Additionally, self-reported data can be prone to bias. Nonetheless, these findings highlight a critical need for integrating DV training modules into physiotherapy education curricula, both at the entry-level and throughout continuing education programs. The Online Professional Education for Physiotherapists and other healthcare professionals on DV (OPEP) project, in response to this gap, has developed a comprehensive online training resource specifically for physiotherapists on DV identification and referral protocols. This initiative aligns with the growing recognition of healthcare professionals’ role in combating DV and can play a vital role in supporting victims and survivors.

Recovering 3D salt dome by gravity data inversion using ResU-Net++

In geophysical research, gravity-based inversion is essential for identifying geologic anomalies, mapping rock structures, and extracting resources such as oil and minerals. However, traditional gravity inversion methods face challenges, such as the volumetric effects of gravity fields and the management of large complex matrices. Unsupervised learning techniques often struggle with overfitting and interpreting gravity data. This study explores the application of various U-Net-based network architectures in gravity inversion, each offering distinct challenges and advantages. Nested U-Net, although effective, requires a high parameter count, leading to extended training periods. The recurrent residual U-Net’s implicit attention mechanism restricts its dynamic adaptability, whereas the attention U-Net’s lack of residual connections raises concerns about gradient issues. This research comprehensively analyzes the training processes, core functionalities, and module distribution of these networks, including the residual U-Net++. Our synthetic studies compare these networks with traditional focused regularized gravity inversion for reconstructing density anomalies. The results demonstrate that the nested U-Net closely approximates the actual model despite some redundancy. The recurrent residual U-Net indicates an improved alignment with minimal redundancies, and the attention U-Net is effective in density prediction but encounters difficulties in areas of low density. Notably, the residual U-Net++ excels in inversion modeling, achieving the lowest misfit percentage and accurately replicating density values. In practical applications, the residual U-Net++ impressively reconstructs the F2 salt diapir in the Nordkapp Basin with well-defined boundaries that closely match seismic data interpretations. These results underscore the capabilities of the residual U-Net++ in geophysical data analysis, structural reconstruction, and inversion, demonstrating its effectiveness in simulated settings and real-world scenarios.

Process for Improving the Quality of Digital Literacy Education for Bankers in Timor Leste

Digital literacy is crucial for economic and social development, particularly in regions like Timor Leste aiming to enhance educational standards among professionals such as bankers. This study aims to investigate the process of improving digital literacy education for bankers in Timor Leste, focusing on enhancing their skills in using digital tools and platforms effectively. The research adopts a qualitative approach, using interviews and surveys to gather insights from bankers regarding their current digital literacy levels and educational needs. Data analysis will involve thematic analysis to identify key areas for improvement. Initial findings indicate a significant gap in digital literacy among bankers in Timor Leste, highlighting a need for tailored educational programs. Recommendations include the development of specialized training modules focusing on digital security, data analytics, and digital communication tools. In conclusion, enhancing digital literacy among bankers in Timor Leste is essential for their professional growth and the economic development of the region. Effective educational interventions can empower bankers with the necessary skills to navigate the digital landscape competently and contribute to sustainable development goals.

Development of sport policy and practice recommendations for pregnant, postpartum and parenting Canadian high-performance athletes

Our objective is to describe the development of evidence-based policy and practice recommendations for pregnant, postpartum and parenting Canadian high-performance athletes. A community-based participatory research approach was employed as the...

A visual management and augmented-reality-based training module for the enhancement of short and long-term procedural knowledge retention in complex machinery setup

Proper training for operators is essential, given the complexity of modern manufacturing and the challenges posed by diverse worker demographics and high workforce turnover. Since conventional training methods are inadequate when considering the mentioned challenges, digital technologies and assistive devices can be used to improve information management. This work studies how the combination of Augmented Reality (AR) and Visual Management (VM) supports worker training by considering short- and long-term efficacy. The literature gaps on researching the training and knowledge retention of manufacturing workers using combined lean tools and digital technologies indicate that current studies focus on short-term training performed in laboratories, overlooking the integration of VM and AR for industrial training. Thus, the authors developed and validated in practice a VMAR-based training module for the setup of complex machinery in the process industry. To assess its efficacy, a comparison between a test group and a confirmation group of company’s workers has been performed. The number and types of setup error have been collected and analysed to evaluate and compare short- and long-term procedural knowledge retention. The results reveal that the VMAR-based training module better supports workers both in the short- and long-period compared with traditional working procedures. Furthermore, the analysis demonstrates the inefficacy of traditional approaches in properly supporting the training of complex machinery setups considering the study’s application. Future works should address the assessment of knowledge retention by considering a longer period, as well as on physiological data collection to better understand which elements of the training module are most crucial to the transfer of knowledge.

Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D Mesh.

The analysis of 3D meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2D images to 3D meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GDSV). The key to the GDSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GDSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training; and be applied to tasks such as shape classification, part segmentation, and 3D scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

Structured Online Systematic Review Training for Medical Students: An Elective Module in Physiology.

Background Recently, elective posting has been introduced by the National Medical Commission (NMC) of India in the undergraduate competency-based medical education (CBME) curriculum. To successfully implement it in medical colleges, facilitators (medical teachers) need to commit extra time. Hence, this study aimed to evaluate the impact of online teaching-learning methods for implementing an elective module for undergraduate medical students at Burdwan Medical College and Hospital, West Bengal, India. Methods An online module for systematic review methods was developed using the Delphi method. This module was used to train 30 medical students divided into six groups. One resident and one facultyfacilitated each group. After the elective program of 15 days, program feedback and evaluation using the Kirkpatrick model were obtained from the students. Results A total of 30 undergraduate medical students with a mean age of 22.7±0.95 years participated in the study. All of them successfully conducted a systematic review per group. The students' feedback was 86.33% positive, and the project evaluation showed an 84% positive opinion. The highest score was for understanding, facilitators' knowledge, and experience. The lowest score was for the immediate applicability of the knowledge. Conclusion An online systematic review training module can be used for elective teaching-learning for final-year medical students, particularly within limited time and resource constraints. Students appreciated the module's clear objectives, appropriate complexity, and facilitators' expertise, leading to improved communication, engagement, and critical-thinking skills. Despite some limitations, these findings suggest that online learning can complement traditional methods and address logistical challenges in medical education, warranting further research on its long-term impact and broader applicability.

Lesson Learned from Post-Disaster Psychosocial Support for Children in Palu, Central Sulawesi—Indonesia

In the aftermath of the devastating Palu disaster in September 2018, communities, especially children, suffered deep psychological trauma. At the request of faith leaders, local NGO Wahana Visi Indonesia (WVI), in partnership with Krida Wacana Christian University (UKRIDA), conducted a psychosocial support training program for volunteers, including Qur’an recitation teachers and Sunday school teachers in Palu. These trained volunteers then initiated a psychosocial support initiative for children in the area. Years later, WVI and UKRIDA conducted a reflective study of the program, collecting data from 40 volunteers through focus group discussions and surveys. The study identified key themes, including the effectiveness of the training, reflections on program implementation, lessons learned, and recommendations for future programs. The study underscores the importance of multi-stakeholder involvement, particularly that of faith-based organizations, in providing psychosocial support in disaster-affected areas. Given the recurring threat of disasters in the region, the study advocates for transparent collaboration among various social stakeholders. To build community resilience, disaster management strategies need to be widely disseminated, and the need for continuous training to strengthen community preparedness is emphasized. In addition, the development of culturally sensitive training modules tailored to the community’s language, age, and religious background is essential. Such a comprehensive commitment will ensure the community’s ability to deal effectively with future disasters. Detailed findings are discussed further in this paper.

Integrating Trauma-Informed Clinical Interviewing Skills in Medical Education for Screening Domestic Violence and Abuse.

A pre-post-training workshop was conducted among 22 postgraduate trainees of the Psychiatry and Emergency Department of a public sector hospital in Karachi. The workshop aimed to build the capacity of postgraduate trainees in trauma management by providing training on screening, clinical interviewing skills, and Psychological first aid to those exposed to domestic violence (DV). The effectiveness of each training module was evaluated by conducting a brief assessment before and after each module and scores were compared using the Wilcoxon signed-rank test. The post-test showed significant improvement in participants' knowledge on screening of domestic violence and trauma symptoms (p = 0.026) and psychological first aid (p <0.001). Postgraduate training programmes need to integrate trauma screening and management education, to prepare healthcare professionals to deal with sensitive clinical presentations at their work settings. Key Words: Postgraduate trainees, Trauma, Domestic violence, Medical education, Workshop.

Pilot evaluation of the effectiveness of an ergonomics awareness educational programme addressed to middle-school children.

Ergonomics programmes addressed to children fit the health-promoting schools (HPS) framework. Beyond the integration of ergonomics in curricula, an important aspect is the effectiveness of the programme. This pilot study aims to evaluate the effectiveness of the programme proposed to a sample of middle-school children (aged 10-13 years) of the metropolitan city of Cagliari. The programme was designed along the lines of the INAIL (Italian National Institute for Insurance against Accidents at Work) scheme and proposed to 260 children. It consisted of three phases: 1) assessment of students' background with a pre-intervention questionnaire (completed by 142 students); 2) teaching of the ergonomics training modules and evaluation of the weight of a sample of 160 backpacks, and of students' perception to be able to sit by putting their feet on the ground; 3) evaluation of conceptual understanding of ergonomics concepts with a post-intervention questionnaire (completed by 107 students out of the 142 students who completed phase 1). There was a substantial improvement in the percentage of correct responses post-intervention, confirmed by the chi-square test, regarding concepts about ergonomics, backpack weight and handling. Most of the backpacks exceeded the maximum allowed value, and 20% of students were perceived to be unable to sit putting their feet on the ground. The practical application of ergonomics pedagogy proved effective in the school setting and should be integrated into school curricula. The prevention technicians can offer a holistic approach, while the teachers are essential to implement the programme on a large scale. Effectiveness evaluation is necessary to guide resource allocation and implement and sustain the HPS approach. A tailored programme has a greater chance of reaching its target audience and maximizing effectiveness.