Additional Training Research Articles

Characteristics associated with gait speed improvements from walking interventions for older adults: A responder analysis

BackgroundSome older adults benefit to a great degree from walking interventions and others experience little improvement. Understanding the personal characteristics associated with greater treatment response to different interventions could assist clinicians in providing patients with matched interventions to optimize both outcomes and resource use. Research questionWhat personal characteristics are associated with improved gait speed for older adults participating in walking interventions? MethodsThis was a secondary analysis of 236 older adults from a trial comparing “Standard” (lower-extremity strength and walking endurance) to “Plus” (additional task-specific training for walking) interventions on gait speed in older adults (≥65 years). Predictors included sociodemographic characteristics, health status, physical performance, and self-reported function. We fitted linear regression models to 12-week change in gait speed. ResultsPredictors of improved gait speed in Standard group included: younger age (β=-0.015), lower BMI (β=-0.005), slower gait speed (β=-0.015), longer Figure 8 Walk time (β=0.010), and higher Late Life Function and Disability Instrument scores (β=0.003). The parsimonious set of multivariable predictors were never married (β=0.081), not a caregiver (β=0.208), no cancer history (β=-0.052), slower chair rise times (β=0.010), slower gait speed (β=-0.021), and better overall function and disability (β=0.006).Predictors of improved gait speed in Plus group included: lower BMI (β=-0.004), farther Six-Minute Walk distance (β=0.014), and greater modified Gait Efficacy Scale (β=0.002). The parsimonious set of multivariable predictors were increased age (β=0.026), no cardiovascular disease (β=0.137), greater total physical activity counts per day (β=0.003), slower baseline gait speed (β=-0.072), and longer Six-Minute Walk distance (β=0.054). SignificanceThose with the combination of suboptimal physical performance and strong self-report of function may benefit from standard strength and conditioning. Individuals may best respond to task-specific training when health status and physical performance are suboptimal and not overtly compromised. Matching interventions with personal characteristics may enhance efficacy of treatments to improve walking in older adults.

Efektivitas Penerapan Model Pembelajaran Problem-Based Learning dalam Pendidikan Agama Islam Siswa SMAN 10 Batanghari

This study evaluates the effectiveness of implementing the Problem-Based Learning (PBL) method in Islamic Religious Education (PAI) teaching for 10th-grade students at SMAN 10 Batanghari. The primary goal of this research is to enhance students' understanding, engagement, and collaboration skills through a more interactive and contextual approach. Data were collected through in-depth interviews, participatory observations, and document analysis, and analyzed using thematic analysis techniques. The results indicate that the application of the PBL method positively impacts students' understanding of PAI material, particularly on the topic "Avoiding Free Association to Protect Human Dignity and Honor." Furthermore, this method also increases student engagement and motivation, as well as strengthens their collaboration and communication skills. However, the study also identifies several challenges, including students' difficulties in time management and independent information-seeking, as well as limited access to technology and resources. These findings suggest that to maximize the effectiveness of PBL, additional teacher training and adequate resource provision are required. This research makes a significant contribution to the development of more effective and relevant PAI teaching strategies that meet current students' needs.

Assessing Attitudes and Confidence in Managing Erectile Dysfunction Among Urology, Internal Medicine, and Family Medicine Providers

ObjectiveTo assess and contrast attitudes and confidence regarding erectile dysfunction (ED) treatment among urologists (GU) and primary care providers (PCPs), identifying areas for collaboration, practice improvement, and additional training to enhance men's healthcare (MH) quality and access. MethodsAn anonymous survey with 25 questions on ED treatment, rated on a 5-point Likert scale, was conducted among our institution's providers in GU, internal medicine (IM), and family medicine (FM). Data were analyzed using descriptive statistics and Fisher’s exact test. Results138 providers responded, including 58 IM, 47 FM, and 33 GU. FM and IM responses generally overlapped with some differences. IM were more positive regarding vacuum erection device (VED) and inflatable penile prosthesis (IPP) satisfaction and risk.Comparing GU to FM + IM, GU displayed greater confidence and knowledge about ED. Notably, 63% of GU used the validated International Index of Erectile Function (IIEF) compared to 3% of FM + IM. Additionally, 100% of GU strongly/agreed that good options remained after pills failed vs 83% of FM + IM.Significant differences in GU vs FM + IM perspectives on IPP included patient willingness to undergo surgery for ED (67% vs. 12%), patient satisfaction (91% vs. 28%), partner satisfaction (79% vs. 26%) and willingness to recommend/repeat surgery (82% vs. 17%). Notably, 82% of GU vs 10% of FM + IM were aware that Medicare covered implants. ConclusionThis survey reveals significant knowledge gaps in ED treatment between GU and PCPs. Targeted provider education, particularly about penile prostheses, could optimize patient care.

A Knowledge Distillation Method Based on Evidence Theory to Prevent Catastrophic Forgetting

Abstract Incremental learning is an emerging machine-learning approach designed to prevent catastrophic forgetting while learning a new task with knowledge retained from previous tasks. However, existing methods often must fully utilize the distributional information of the old task model’s outputs. To alleviate this, we propose a class incremental learning method grounded in evidence theory to leverage the distributional information of outputs from the old task model, which integrates uncertainty estimation into the knowledge distillation process to ensure the new task model effectively learns from the old task model’s distribution information. Specifically, we incorporate uncertainty estimation based on evidence theory to calculate knowledge distillation loss during training. We compute the uncertainty estimates of outputs from both the new and old task models, and then they are used in the knowledge distillation loss calculation. We propose a novel classification strategy that considers outputs’ probability and uncertainty estimates, which could determine the sample category without requiring an additional training phase or supplementary models. Experiments on the CIFAR-100 and ImageNet datasets demonstrate the effectiveness of the proposed method, showing that it outperforms existing methods by improving accuracy by 1%-2%. Results suggest that leveraging the distribution information of model outputs can effectively mitigate catastrophic forgetting in deep learning models within incremental learning scenarios.

Oncology nurses’ assessment of providing equitable care for patients with developmental disabilities.

164 Background: Access to quality health care can be challenging, especially for people with developmental disabilities (PwDD). Developmental Disabilities (DD) occur before age 22 and include physical, learning, language, or behavioral impairments. Studies of training and experience of oncology nurses (RNs) who care for PwDD are limited. The purpose of this study was to explore to what extent RNs working in a community oncology practice are prepared to provide quality patient-centered care to patients with DD. Methods: As no tool exists to capture oncology RNs experiences caring for PwDD, a survey was developed with input from a group of oncology nurse experts. Both academic IRB and Privacy Board approvals were completed prior to recruitment. A Qualtrics survey link was shared with RNs and Advanced Practice RNs via email. Questions about nurses’ background, perceptions of clinic resources and accommodation and referral to clinical trials were included in the survey. Nurses were also presented with 3 scenarios of oncology patients who have either been blind since birth, have autism or cerebral palsy. RN respondents were asked how to care for them, what challenges might be encountered, and what resources would be available to them. Results: Usable surveys were completed by 122 RNs. 77 RNs cared for PwDD in a statewide Oncology practice, 81 RNs had previously cared for PwDD in any setting and 54 RNs have friends or family members with DD. Only 7 RNs reported no experience with PwDD. Seven nurses knew of PwDD who had participated in clinical trials. Themes identified through the qualitative analysis include Accommodations, Strategies, Communication and Referrals. Conclusions: This study provides an initial step in understanding how to reduce disparities for oncology patients with DD. Key elements revealed additional training needs, standardized facility accommodation needs, and advocacy for clinical trials participation. Through a team approach and engagement with patient advocacy groups, the challenge of engaging PwDD in clinical trials will be tackled. More training and resource education for RNs and staff regarding care of PwDD will be built and disseminated through a standardized process to bridge gaps to impact delivery of care.

Research on Fracture Parameter Optimization Method Based on Generative Adversarial Network in Small Sample Size

Abstract This study addresses the optimization of fracturing parameters in the fractured gas reservoirs of the Tarim Basin, especially under the challenge of small sample sizes and high data acquisition costs. It proposes an innovative method based on Generative Adversarial Networks (GAN). Traditional fracturing technology parameter optimization often leads to model overfitting and poor generalization due to limited data. This research adopts GAN for data augmentation to generate additional training samples, thereby improving the training effect of machine learning models. The combination of genetic algorithms and model fusion techniques further optimizes production capacity prediction and fracturing parameter adjustment. Application of this method to actual data from the Tarim Basin shows that it effectively enhances the accuracy of parameter optimization and the generalization ability of the model, providing significant guidance for gas reservoir development under similar complex geological conditions.

Handheld ultrasound versus standard machines for placement of peripheral IV catheters: A randomized, non-inferiority study

IntroductionUltrasound guided IV catheter (USGIV) access occurs frequently in Emergency Departments (EDs). This task is often performed using large, expensive, cart-based ultrasound systems (CBUS) which are frequently needed for other ED ultrasound functions and can be cumbersome to use and store. Handheld ultrasounds (HHUs) may be able to meet this need, but it is unknown if they function interchangeably with CBUS for USGIV placement. We performed a prospective, randomized, noninferiority study to compare the success rate of HHUs to CBUSs for placing USGIVs. MethodsED patients 18 and older needing an USGIV were approached for enrollment and randomized to receive an USGIV placed by CBUS or HHU. USGIVs were placed by any ED physician or nurse trained in placement. A placement was considered attempted upon needle entry into the skin. An USGIV was successful if it was immediately flushable with saline. Data was collected on the success of IV placement, number of attempts, IV and provider characteristics, patient demographics, and length of time the USGIV lasted. Demographics and operator and IV characteristics were analyzed using Pearson's Chi square, Fischer's Exact test, or Wilcoxon rank sum tests. Non-inferiority was assessed using the Farrington-Manning test. Results were approached per protocol and analyzed in R. Results312 patients were enrolled. Patient and IV characteristics were similar between groups. There was no difference in the number of successful USGIVs placed in either group (p≥0.9) with 146 in the CBUS group and 145 in the HHU group. There was no difference in the first attempt success rate between groups (p = 0.8) and HHU was noninferior to CBUS for successful USGIV placement (p = 0.0001). The rate of premature USGIV failure was similar between HHU and CBUS (4.0 % and 6.7 %). ConclusionHHU was noninferior to CBUS for successful USGIV placement. There was no difference in the rate of first attempt success at placement or USGIV survival to a patient's ED disposition between groups. No significant additional training was required for ED providers of all levels to use the HHUs.

Fine-grained detoxification framework via instance-level prefixes for large language models

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, their practical usability is often compromised by a propensity to generate toxic content, such as insults, threats, and profanity, particularly in response to adversarial prompts. Several fine-tuning and decoding approaches have been employed to address this challenge to mitigate toxicity. Nevertheless, these methods typically necessitate additional resources, such as high-quality training data or auxiliary models, thereby incurring extra costs. In this paper, we propose a novel detoxification framework, Fine-Grained Detoxification via Instance-Level Prefixes (FGDILP), which effectively mitigates the generation of toxic text without incurring additional training costs. Specifically, FGDILP leverages contextualized representations in attention space by contrasting a positive prefix-prepended prompt against multiple negative prefix-prepended prompts at the instance level. This methodology facilitates the construction of fine-grained subtoxicity vectors, which are subsequently fused to adjust the original generation pathway when responding to raw prompts. Our results demonstrate that FGDILP enables controlled text generation concerning detoxification at both the utterance and context levels. While our method slightly impacts generation fluency and diversity, it significantly outperforms prompt-based baselines regarding detoxification effectiveness. Our code is available at https://github.com/xinykou/FGDILP.

Disparate Digital Literacy Levels of Nursing Manager and Staff, Specifically in Nursing Informatics Competencies and Their Causes: A Cross-Sectional Study.

The digitalization of healthcare is developing quickly. Every care provider needs to be up-to-date on this technology. The purpose of this research was to identify the digital literacy of nursing staff and nurse managers. A cross-sectional study assessed nurses' digital literacy, specifically with regard to nursing informatics competencies in hospitals, primary healthcare, and clinics. The study sample included 100 nurses and 83 nurse managers. Researchers applied a modified questionnaire on nurses' informatics competencies from TIGER Nursing Informatics, with a validity range of 0.497-0.897 and a reliability value of 0.975. Participants showed an acceptable level of computer literacy, although their scores were below 80%. Their scores were unrelated to workplace, duration and type of technology used, or gender. Nursing informatics competencies correlated significantly with age and work experience (p = 0.001). Age correlated negatively with technical computer skills but positively with competence in the implementation of healthcare. There was a positive link with a nurse's position and education level, indicating that nurses with more education acquire higher levels of informatics competencies than those with less education. Additionally, those in higher positions tended to possess more advanced digital competence. Nurse managers play a significant role in training nursing staff members to enhance their informatics competencies. This study's findings underscore the need to raise staff members' informatics competencies by providing additional training.

A Mixed Methods Evaluation of Pharmacists' Readiness to Provide Long-Acting Injectable HIV Pre-exposure Prophylaxis in California.

Pre-exposure prophylaxis (PrEP) uptake remains low among people who could benefit, some of whom may prefer alternatives to oral PrEP, such as long-acting injectable pre-exposure prophylaxis (LAI-PrEP). We evaluated the potential for LAI-PrEP provision in pharmacies through a mixed methods study of pharmacists in California, where Senate Bill 159 enables pharmacists to independently provide oral PrEP. In 2022-2023, we conducted an online cross-sectional survey of California pharmacists and pharmacy students (n = 919) and in-depth interviews with pharmacists (n = 30), both of which included modules assessing attitudes about PrEP provision. Using log-binomial regression, we estimated prevalence ratios (PRs) comparing survey participants' willingness to provide LAI-PrEP by pharmacy- and individual-level characteristics. Qualitative interview data were analyzed using Rapid Qualitative Analysis to identify factors that may affect pharmacists' provision of LAI-PrEP. Half of the survey participants (53%) indicated that they would be willing to administer LAI-PrEP using gluteal injection in their pharmacy. Willingness was higher among participants who worked in pharmacies that provided vaccinations or other injections (56% vs. 46%; PR: 1.2; 95% confidence interval: 1.0-1.4) and/or oral PrEP under Senate Bill 159 (65% vs. 51%; PR: 1.3; 95% confidence interval: 1.1-1.5) than among participants whose pharmacies did not. Interviewed participants reported barriers to LAI-PrEP provision, including the need for increased training and staffing, a private room for gluteal injections, better medication access, and payment for services. Pharmacies offer a promising setting for increased LAI-PrEP access. However, pharmacists may require additional training, resources, and policy changes to make implementation feasible.

Behavior intention in coping cloud adaptation

This study investigates the indirect impact of perceived technology complexity on the adoption of cloud platforms by data scientists in Brazil. Specifically, it examines the mediating role of perceived work-related opportunities and behavioral intentions, as well as the use of exploration-to-innovate coping strategies. The research employs an online questionnaire survey and uses Confirmatory Factor Analysis, SEM, and OLS to analyze the data. The findings suggest that a serial mediation effect focused on behavioral aspects can better explain the adoption of cloud computing technology. The study identifies the need for further investigation into the potential moderating effects of voluntariness and self-efficacy on the relationship between perceived technology complexity and cloud platform adaptation. Moreover, incorporating other exogenous constructs such as new adaptation strategies, job outcomes, and job satisfaction may provide a more comprehensive understanding of the factors influencing technology adoption in the context of data science. The study highlights that reducing technology complexity can lead to increased adoption, better user experience, better retention rates, more innovation possibilities, and better competition position. However, additional training and support requirements for data scientists can increase the cost and time required for onboarding and maintenance. A cloud platform that is easy to use can enable remote work and collaboration, opening up opportunities for job growth and flexible work arrangements. This can be particularly beneficial for Brazilian professionals who have strong innovation skills but limited training and support. By transcending geographic barriers and infrastructure limitations, a user-friendly cloud platform can help bridge the digital divide. On the other hand, a complex cloud platform can widen the digital divide, particularly in developing countries, exacerbating existing inequalities. The research contributes to theory building by providing a serial mediation framework that better explains technology adaptation phenomena by combining coping theory and technology acceptance model.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

The sphere of exposure: centering user experience in community science air monitoring

Community science has increased in popularity in communities where residents hope to investigate the relationship between environmental issues and personal health. This study partnered with neighborhoods in the most polluted residential zip code in the US to conduct community science air quality monitoring. We conducted 60 semi-structured interviews after two monitoring deployments to understand participants’ subjective experiences of pollution exposure, their engagement with low-cost air quality monitors, and their data interpretation. We utilize the environmental health concept ‘exposure experience’ to analyze how participants use personal monitors, understand their data, and reinterpret their pollution exposure as a result. We further explore how participants’ understandings are circumscribed by the technological features of low-cost monitors. We find that participants adopt both protective and mitigating behavioral changes based on information gained from personal experiments and hypothesis testing while using the monitors. Of their own accord, 40% of participants in this study adopted mitigation behaviors after identifying sources that impacted their personal air quality. Our analysis reveals that real-time data accessibility through low-cost monitors builds exposure awareness and enables residents of environmental justice communities to test, validate, or invalidate sensory experiences and challenge existing assumptions. These findings point to specific pathways for using low-cost monitors to support individual decision-making and contribute to behavioral change. Findings also identify some limitations of low-cost monitors; designers of low-cost monitors should consider how composite Air Quality Scores may encourage community scientists to equally value scientifically-established pollutants (e.g., PM) with less scientifically-established pollutants (e.g., TVOCs), without additional scientific training and health-related information.

Psychological preparedness in disaster management: A survey of leaders in Saudi Arabia's emergency operation centers

This study assesses perceptions of psychological preparedness among leaders in Emergency Operation Centers (EOCs) across Saudi Arabia. Effective disaster management requires managing stress, maintaining emotional stability, and adapting to rapid changes. A descriptive survey was conducted in 2024 with 110 leaders from various Health regions and the National Health EOC, achieving an 86.4 % response rate. Participants completed a structured questionnaire assessing their confidence in managing stress, the effectiveness of stress-relief techniques, and emotional stability. Results indicated high confidence in stress management (mean score 4.19, SD = 0.88) and emotional stability (mean score 4.27, SD = 0.80), along with moderately high ratings for the effectiveness of stress-relief techniques (mean score 3.64, SD = 0.99). Only 30.53 % had received formal training on psychological preparedness, while 96.84 % expressed a need for additional training. The study found significant correlations between confidence, emotional stability, and the regular review of coping strategies. These results highlight the critical need for comprehensive training programs to improve the psychological preparedness of disaster management personnel.

Компетенции специалистов, работающих с детьми с расстройствами аутистического спектра, в системе непрерывного образования

This paper identifies the competencies of specialists working with children with Autism Spectrum Disorder (ASD) using a modular competence approach in the continuing education system. The study provided additional training to specialists in Tatarstan and conducted an online survey of 104 participants who had completed advanced training. The survey identified an uneven formation of competencies among specialists, with teachers and educators less prepared to interact with children with ASD than speech therapists and psychologists. Inadequacies were found in all groups, especially in diagnosis and technology applications. A model of additional education was developed for specialists in the integrated support system for children with ASD that needs to be expanded to include teachers and educators involved in inclusive education.

A Qualitative Evidence Synthesis Of The Perceptions And Experiences Of Animal Assisted Interventions For People Living With Dementia

Abstract Background Animal assisted intervention (AAI) is a popular type of non-pharmacological psychosocial intervention, which has a potentially positive effect on physical, psychological, and social functioning for people with dementia. However, there is a lack of evidence regarding the experiences and factors of implementing AAI, and its impacts on people with dementia in different contexts. This review aims to synthesise key stakeholders’ experiences and perceptions of AAI for people with dementia in community settings. Methods We systematically searched Medline, CINAHL, Embase, Scopus, Web of Science, PsycINFO and AgeLine for potentially eligible studies. Thematic analysis was used to synthesise the data from included records. We assessed the methodological limitations using an adaptation of the Critical Appraisal Skills Programme (CASP) and used GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative research) to assess confidence in the review findings. Two Patient and Public Involvement (PPI) contributors were involved to enhance the quality and relevance of the research. Results We included 14 reports and developed three analytical themes, incorporating a gardening analogy: planting-connecting with animals, growing-engaging in AAI, and nurturing-making AAI work, with 15 key findings. Our review highlights that positive experiences, growing connections, and potential benefits were perceived within AAI. A multicomponent AAI can provide various sensory inputs, attributing to a rich experience. Flexible and individualised AAI tailoring to people’s needs and preferences is important. Additional training and education for staff are necessary to facilitate the intervention, reduce risks, and nurture connections. Conclusion This review describes people’s experiences and perceptions of AAI for people with dementia, and provides recommendations on development and implementation of AAI, with moderate to high confidence in its findings. It is important to consider the factors that influence the implementing AAI identified in this review, to facilitate engagement and long-term impacts in future interventions.

Vestibular Assessment In Patients With Self-Reported Dizziness And A History Of Falls

Abstract Background World Falls Guidelines recommend that any patient with a history of falls and reporting dizziness should be screened for Benign Paroxysmal Positional Vertigo (BPPV) (Montero-Odasso et al.,2022). Diagnosing and treating BPPV in particular has been shown to reduce the incidence of falls in older adults, however, it is widely underdiagnosed (Li et al., 2023). This audit explored compliance with falls guidelines in a day hospital setting where falls work up assessments take place on older patients (75 years and older). It aimed to identify reasons why vestibular assessments may not have been undertaken and establish methods to improve compliance with the undertaking of vestibular assessments where indicated. Methods A retrospective chart was undertaken for patients who attended a day hospital service for older adults between January and April 2023. A Microsoft excel sheet was used to track data. All data was anonymised and stored on a password encrypted HSE networked drive accessed in the day hospital. Results A total of 97 charts were reviewed, with 31 patients reporting to have experienced dizziness with falls. For 33% of those patients (n=10), a vestibular screen was not carried out. For 7 out of 10 of these patients a screen was not carried out due to insufficient staff training in the area, with one patient not having been referred to physiotherapy, also secondary to insufficient staff training. Conclusion Facilitating adequate access to training in vestibular rehab for healthcare professionals is an essential part of ensuring BPPV and other vestibular disorders are diagnosed and treated in patients with self-reported dizziness and falls. Training as part of undergraduate degrees could be considered to ensure BPPV is diagnosed and treated in a timely manner. Additional staff training was provided in the day hospital setting in question and a re-audit of vestibular screening will take place in summer 2024.

Developing An Online Course To Increase Staff Awareness And Responses To Unwanted And Illegal Sexual Behaviour Perpetrated Against Older People

Abstract Background The idea that older people can be victims of unwanted sexual behaviour or sexual assault in health and social care settings is relatively recent. In the Irish context, 165 reports of sexual abuse of older people were made to the HSE National Safeguarding and Protection teams in 2022. The prevalence of sexual abuse against older people in health and social care settings is poorly understood and there is an acknowledgement that hospital staff require additional training to understand and manage incidents. Methods In response to the need for training and support for staff, an online awareness-raising course based on a pre-developed course from Australia ‘Preventing unwanted sexual behaviour against older people in health and social care settings’ will be adapted, trialled and tested in a large acute teaching hospital. This course sets out to support acute care staff to identify and manage incidents of sexualised behaviour, to seek expert help and to ensure that older people are protected from unwanted and at times, what may constitute illegal sexual behaviour. Results The adapted course ‘Understanding and Responding to Unwanted or Illegal Sexual Behaviour Perpetrated against Older People in Health and Social Care Settings’ utilises a trauma-informed lens and key themes covered include defining unwanted sexual behaviours, understanding older people and sexual behaviour, risk factors for sexual abuse, barriers to recognising and reporting abuse and managing incidents. Conclusion The ‘trial and test’ will involve a feedback survey (N=50 respondents) and focus groups with participating healthcare professionals from the hospital site informing further refinement of the course content in preparation for wider rollout and implementation across Ireland's health and social care settings.

Early intervention speech-language pathologists’ beliefs and practices related to assessing dual language learners

IntroductionEarly intervention (EI) speech-language pathologists (SLPs) are required to provide culturally and linguistically responsive assessments for dual language learners (DLLs). However, SLPs consistently report feeling underprepared to assess DLLs and research demonstrates gaps in implementation of best practices in pediatric outpatient and school-based settings. This study was designed to understand EI SLPs’ beliefs and practices related to assessing DLLs referred to early intervention programs. MethodsA total of 132 EI SLPs completed a survey in which they were asked to describe their assessment procedures for a DLL case scenario and were further asked to identify the degree to which they agreed with a variety of assessment practices for assessing DLLs. ResultsResults revealed that EI SLPs’ beliefs aligned with best practices for assessing DLLs. However, substantial gaps exist between EI SLPs’ beliefs and their self-reported practices for assessing DLLs. ConclusionsThese results suggest DLLs may not be receiving evidence-based EI assessments, leading to the potential perpetuation of both over- and under-diagnosis of speech and language delays within this population. Implications include the need to increase the quality of EI SLPs assessment practices through additional training, the removal of systems-level barriers, and the continued need for increased training within preprofessional training programs.

IMCSN: An improved neighborhood aggregation interaction strategy for multi-scale contrastive Siamese networks

Graph contrastive learning is an effective method for enhancing graph representation by maximizing the similarity of representations between analogous graphs while minimizing the similarity between dissimilar ones. A common approach to improve the representation capabilities of graphs involves data augmentation, which generates additional training samples through transformations applied to the original graphs. However, traditional data augmentation techniques, such as random deletion of nodes or edges, often compromise the structural integrity of graphs, result in loss of crucial information, and lead to representation instability. To overcome these drawbacks, this study introduces a novel data augmentation paradigm, the integration of directed random noise (IDR), which incorporates controlled random noise to optimize the augmentation objectives. IDR enhances the diversity and robustness of representations without sacrificing the structural integrity of the graphs. This approach significantly improves the performance of graph contrastive learning, avoiding the common issues of graph structure damage and information loss associated with conventional methods. To further refine the model, this paper proposes an improved multiscale contrastive Siamese network framework that employs three Siamese networks to process different views of input graphs. It utilizes cross-network and cross-view contrastive learning objectives to optimize graph representations, leveraging the complementary information between different views to maximize the consistency of representations among similar graphs. This enhancement improves the quality and generalizability of graph representations. Additionally, the introduction of a self-supervised loss function based on graph reconstruction within the loss framework capitalizes on the structural similarity between the original and reconstructed graphs to further refine graph representations. This loss function ensures that the global view retains more structural information from the original graph, thus enhancing the complementarity between the global and local views. The effectiveness and stability of this research framework are demonstrated through node classification tasks and visualizations on six real-world datasets, comparing favorably against existing methods such as MERIT and GraphVAT. The proposed model achieves accuracy improvements of 3.21 % and 3.71 % on the Cora dataset and 1.12 % and 1.42 % on the CiteSeer dataset. Additionally, it ranks first in an average accuracy comparison across six datasets, with scores of 80.6 % and 52.67 %. These results underscore the robustness and efficacy of the proposed model in self-supervised graph representation learning, offering substantial advancements over existing techniques.