Clinical nurses' experiences about "breaking bad news" during the COVID-19 pandemic: A qualitative study.

BACKGROUND: Nurses have the most contact with COVID-19 patients and their families, while it is unclear how nurses react when they give bad news during pandemic disaster, particularly in the cultural and social context of Iran. So, our main purpose was to explore the experiences of clinical nurses about breaking bad news (BBN) in the context of the COVID-19 epidemic era. MATERIALS AND METHOD: The study was a qualitative content analysis approach. Data were collected by the purposive sampling method through in-depth interviews with 13 nurses in Isfahan University of Medical Sciences. The method of data analysis was conventional qualitative content analysis. RESULTS: The participants of this study were 13 nurses. The work experience range was from 2 to 18 years, and in terms of education, one of them was Ph.D., eight had a bachelor's degree education, and four had a master's degree in nursing. Qualitative data of content analysis were obtained in four main categories such as nurses' avoidance of BBN, considering the patient's and family's beliefs in BBN, nurses' unpreparedness to deliver bad news during the pandemic crisis, and surrender of the patient and family members in the face of the COVID-19 bad news. CONCLUSION: The results of the research showed that due to probability of occurrence of pandemic in the futureand also the nature of the nursing profession, so nurses should be familiar with the correct ways of BBN and existing protocols on crisis conditions and cultural and religious context of the society to provide a high quality of care for patients and their families.

Building a benchmark dataset for the Kurdish news question answering.

This article presents the Kurdish News Question Answering Dataset (KNQAD). The texts are collected from various Kurdish news websites. The ParsHub software is used to extract data from different fields of news, such as social news, religion, sports, science, and economy. The dataset consists of 15,002 news paragraphs with question-answer pairs. For each news paragraph, one or more question-answer pairs are manually created based on the content of the paragraphs. The dataset is pre-processed by cleaning and normalizing the data. During the cleaning process, special characters and stop words are removed, and stemming is used as a normalization step. The distribution of each question type is presented in the KNQAD. Moreover, the complexity of the QA problem is analyzed in the KNQAD by using lexical similarity techniques between questions and answers.

Dissecting the infodemic: An in-depth analysis of COVID-19 misinformation detection on X (formerly Twitter) utilizing machine learning and deep learning techniques.

The alarming growth of misinformation on social media has become a global concern as it influences public opinions and compromises social, political, and public health development. The proliferation of deceptive information has resulted in widespread confusion, societal disturbances, and significant consequences for matters pertaining to health. Throughout the COVID-19 pandemic, there was a substantial surge in the dissemination of inaccurate or deceptive information via social media platforms, particularly X (formerly known as Twitter), resulting in the phenomenon commonly referred to as an "Infodemic". This review paper examines a grand selection of 600 articles published in the past five years and focuses on conducting a thorough analysis of 87 studies that investigate the detection of fake news connected to COVID-19 on Twitter. In addition, this research explores the algorithmic techniques and methodologies used to investigate the individuals responsible for disseminating this type of fake news. A summary of common datasets, along with their fundamental qualities, for detecting fake news has been included as well. For the purpose of identifying fake news, the behavioral pattern of the misinformation spreaders, and their community analysis, we have performed an in-depth examination of the most recent literature that the researchers have worked with and recommended. Our key findings can be summarized in a few points: (a) around 80% of fake news detection-related papers have utilized Deep Neural Networks-based techniques for better performance achievement, although the proposed models suffer from overfitting, vanishing gradients, and higher prediction time problems, (b) around 60% of the disseminator related analysis papers focus on identifying dominant spreaders and their communities utilizing graph modeling although there is not much work done in this domain, and finally, (c) we conclude by pointing out a wide range of research gaps, for example, the need of a large and robust training dataset and deeper investigation of the communities, etc., and suggesting potential solution strategies. Moreover, to facilitate the utilization of a large training dataset for detecting fake news, we have created a large database by compiling the training datasets from 17 different research works. The objective of this study is to shed light on exactly how COVID-19-related tweets are beginning to diverge, along with the dissemination of misinformation. Our work uncovers notable discoveries, including the ongoing rapid growth of the disseminator population, the presence of professional spreaders within the disseminator community, and a substantial level of collaboration among the fake news spreaders.

The depiction of people with schizophrenia in news media: A cross-national analysis.

BACKGROUND: Though people with schizophrenia have been habitually stigmatized in the media, the past two decades have seen a substantial rise in public awareness and anti-stigma intervention plans. AIMS: In this comprehensive cross-national study, we examine the portrayal of people with schizophrenia in the news media across four countries: the U.S., the U.K., Russia, and Israel. METHODS: We employed thematic content analysis to analyze 80 articles from four prominent middle-market and tabloid news media outlets. RESULTS: Findings suggest people with schizophrenia were routinely depicted in the news media as violent and dangerous perpetrators who were typically young adult white males. CONCLUSIONS: Though some differences existed between venues in different countries, this study suggests that despite the rise in public awareness and anti-stigma intervention plans, the media overall - regardless of country origin - mostly failed to deliver the desired anti-stigma results.

Ensemble Techniques for Robust Fake News Detection: Integrating Transformers, Natural Language Processing, and Machine Learning.

The proliferation of fake news across multiple modalities has emerged as a critical challenge in the modern information landscape, necessitating advanced detection methods. This study proposes a comprehensive framework for fake news detection integrating text, images, and videos using machine learning and deep learning techniques. The research employs a dual-phased methodology, first analyzing textual data using various classifiers, then developing a multimodal approach combining BERT for text analysis and a modified CNN for visual data. Experiments on the ISOT fake news dataset and MediaEval 2016 image verification corpus demonstrate the effectiveness of the proposed models. For textual data, the Random Forest classifier achieved 99% accuracy, outperforming other algorithms. The multimodal approach showed superior performance compared to baseline models, with a 3.1% accuracy improvement over existing multimodal techniques. This research contributes to the ongoing efforts to combat misinformation by providing a robust, adaptable framework for detecting fake news across different media formats, addressing the complexities of modern information dissemination and manipulation.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Clinical Outcomes, Costs, and Value of Surgery Among Older Patients with Colon Cancer at US News and World Report Ranked Versus Unranked Hospitals.

BACKGROUND: US News and World Report (USNWR) hospital rankings influence patient choice of hospital, but their association with surgical outcomes remains ill-defined. We sought to characterize clinical outcomes and costs of surgery for colon cancer among USNWR top ranked and unranked hospitals. METHODS: Using Medicare Standard Analytic Files, patients aged ≥65 years undergoing surgery for colon cancer were identified. Hospitals were categorized as 'ranked' or 'unranked' based on USNWR cancer hospital rankings. One-to-one matching was performed between patients treated at ranked and unranked hospitals, and clinical outcomes and costs of surgery were compared. RESULTS: Among 50 ranked and 2522 unranked hospitals, 13,650 patient pairs were compared. Overall, 30-day mortality was 2.13% in ranked hospitals versus 3.68% in unranked hospitals (p < 0.0001), and the overall paired cost difference was $8159 (p < 0.0001). As patient risk increased, 30-day mortality differences became larger, with the ranked hospitals having 30-day mortality of 7.59% versus 11.84% for unranked hospitals among the highest-risk patients (p < 0.0001). Overall paired cost differences also increased with increasing patient risk, with cost of care being $72,229 for ranked hospitals versus $56,512 for unranked hospitals among the highest-risk patients (difference = $14,394; p = 0.02). The difference in cost per 1% reduction in 30-day mortality was $9009 (95% confidence interval [CI] $6422-$11,597) for lowest-risk patients, which dropped to $3387 (95% CI $2656-$4119) for highest-risk patients (p < 0.0001). CONCLUSION: Treatment at USNWR-ranked hospitals, particularly for higher-risk patients, was associated with better outcomes but higher-cost care. The benefit of being treated at highly ranked USNWR hospitals was most pronounced among high-risk patients.

OLTW-TEC: online learning with sliding windows for text classifier ensembles.

In the digital age, rapid dissemination of information has elevated the challenge of distinguishing between authentic news and disinformation. This challenge is particularly acute in regions experiencing geopolitical tensions, where information plays a pivotal role in shaping public perception and policy. The prevalence of disinformation in the Ukrainian-language information space, intensified by the hybrid war with russia, necessitates the development of sophisticated tools for its detection and mitigation. Our study introduces the "Online Learning with Sliding Windows for Text Classifier Ensembles" (OLTW-TEC) method, designed to address this urgent need. This research aims to develop and validate an advanced machine learning method capable of dynamically adapting to evolving disinformation tactics. The focus is on creating a highly accurate, flexible, and efficient system for detecting disinformation in Ukrainian-language texts. The OLTW-TEC method leverages an ensemble of classifiers combined with a sliding window technique to continuously update the model with the most recent data, enhancing its adaptability and accuracy over time. A unique dataset comprising both authentic and fake news items was used to evaluate the method's performance. Advanced metrics, including precision, recall, and F1-score, facilitated a comprehensive analysis of its effectiveness. The OLTW-TEC method demonstrated exceptional performance, achieving a classification accuracy of 93%. The integration of the sliding window technique with a classifier ensemble significantly contributed to the system's ability to accurately identify disinformation, making it a robust tool in the ongoing battle against fake news in the Ukrainian context. The application of the OLTW-TEC method highlights its potential as a versatile and effective solution for disinformation detection. Its adaptability to the specifics of the Ukrainian language and the dynamic nature of information warfare offers valuable insights into the development of similar tools for other languages and regions. OLTW-TEC represents a significant advancement in the detection of disinformation within the Ukrainian-language information space. Its development and successful implementation underscore the importance of innovative machine learning techniques in combating fake news, paving the way for further research and application in the field of digital information integrity.

Evaluation of Guilan University of Medical Sciences medical interns' attitudes regarding breaking bad news to patients based on the SPIKES model, 2020 to 2022.

BACKGROUND: Bad news may be defined as "any information which adversely and seriously affects an individual's view of his or her future." It seems necessary for physicians to use a specific method to break the bad news to patients properly. Due to the importance of this skill and its effects on patient's hope and motivation to continue his treatment process, in this study, we evaluate the interns of Guilan University of Medical Sciences' attitude to breaking bad news (BBN) to the patients based on strategy for BBN, perception of condition or seriousness, invitation from the patient to give information, knowledge: giving medical facts, explore emotions, and sympathize (SPIKES) model in 2020-2022. MATERIALS AND METHODS: In this cross-sectional study in Iran, 153 Guilan University of Medical Sciences interns were selected as a census sample in 2020-2022. A self-administered questionnaire collected the information with standard tests confirming its reliability and validity. The collected data were described and analyzed using Statistical Package for the Social Sciences (SPSS) 16. The Chi-square test was used to measure the statistical relationship between the demographic variables and the entire questionnaire. Also, a one-way analysis of variance (ANOVA) test was used to measure the relationship between the average age and the scores obtained from the four main areas and the entire questionnaire. A statistical level of less than 0.05 was considered significant. RESULTS: 43.1% of the interns were men, and 56.9% were women. The mean attendance age was 26.12 ± 1.32, the minimum age was 23, and the maximum was 33. Only 8.5% of the interns in this study had been taught about BBN, and most participants announced that they feel pressure and anxiety when BBN to patients. The attitude of interns in this study was not satisfying in all four parts of the study: individual preference (54.2% of participants showed poor attitude), preparing environmental conditions for BBN (60.8% of participants showed poor attitude), how to break bad news (52.3% of participants showed poor attitude), and the things that are done after BBN (52.9% of participants showed poor attitude). CONCLUSION: Based on the results, the attitude of the interns who had participated in this study was not satisfactory. Due to the importance of this communication skill to reduce physician anxiety and best control patients' reactions, managing courses in the undergraduate curriculum seems necessary.

Incentivizing news consumption on social media platforms using large language models and realistic bot accounts.

Polarization, misinformation, declining trust, and wavering support for democratic norms are pressing threats to the US Exposure to verified and balanced news may make citizens more resilient to these threats. This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a 2-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing a URL to the topic-relevant section of a verified and ideologically balanced news organization and an encouragement to follow its Twitter account. To test differential effects by gender of the bots, the treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organizations, sharing and liking of news content (determined by our extensive list of news media outlets), tweeting about politics, and liking of political content (determined using our fine-tuned RoBERTa NLP transformer-based model). Although the treated users followed more news accounts and the users in the female bot treatment liked more news content than the control, these results were small in magnitude and confined to the already politically interested users, as indicated by their pretreatment tweeting about politics. In addition, the effects on liking and posting political content were uniformly null. These findings have implications for social media and news organizations and offer directions for pro-social computational interventions on platforms.

Comprehensive dataset of user-submitted articles with ideological and extreme bias from Reddit.

Our study aims to collect data to understand ideological and extreme bias in text articles shared across various online communities, particularly focusing on the language used in subreddits associated with extremism and targeted violence. Initially, we gathered data from related online communities, specifically the r/Liberal and r/Conservative communities on Reddit, utilizing the Reddit Pushshift API to collect URLs shared within these subreddits. Our aim was to gather news, opinion, and feature articles, resulting in a corpus of 226,010 articles. We also curated a balanced subset of 45,108 articles and annotated 4000 articles to validate their relevance, facilitating understanding of language usage within ideological Reddit communities and insights into ideological bias in media content. Expanding beyond binary ideologies, we introduced a new category termed "Restricted" to encompass articles shared in private or banned subreddits. This third category encompasses articles shared in restricted, privatized, quarantined, or banned subreddits characterized by radicalized and extremist ideologies. This expansion yielded a large dataset of 377,144 articles. Additionally, we included articles from subreddits with unspecified ideologies, creating a holdout set of 922,522 articles. In total, our combined dataset of 1.3 million articles collected from 55 different subreddits will assist in examining radicalized communities and providing discourse analysis in associated subreddits, enhancing understanding of the language used in articles shared within radicalized Reddit communities and offering insights into extreme bias in media content. In summary, we collected 1.52 million articles to understand ideological and extreme bias, providing a comprehensive dataset that aids in understanding language usage within text articles posted in ideological and extreme Reddit communities.

Corrigendum: How incidental and intentional news exposure in social media relate to political knowledge and voting intentions.

[This corrects the article DOI: 10.3389/fpsyg.2023.1250051.].

Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning.

Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques-BERT's deep contextual understanding and the generative capabilities of GPT-to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

How to break the news in amyotrophic lateral sclerosis/motor neuron disease: practical guidelines from experts.

In amyotrophic lateral sclerosis/motor neuron disease (ALS/MND), it is necessary to communicate difficult news during the initial diagnosis and throughout the disease trajectory as the condition progresses. However, delivering difficult news to people with ALS/MND is an emotionally demanding task for healthcare and allied health professionals-one for which many feel ill-prepared because of limited training in this area. Ineffective communication of difficult news damages the patient-provider relationship and negatively impacts patient quality of life (QoL). To address this issue, we developed the A-L S-PIKES protocol based on available literature and our extensive clinical experience. It provides easy-to-follow, stepwise guidelines to effectively deliver difficult news to people with ALS/MND (PALS) that includes: Advance Preparation (preparing for the discussion logistically and emotionally); Location & Setting (creating a comfortable setting that fosters rapport); Patient's Perceptions (assessing PALS' understanding and perception of their condition); Invitation (seeking PALS' permission to share information); Knowledge (sharing information in a clear, understandable manner); Emotion/Empathy (addressing emotions with empathy and providing emotional support); and Strategy & Summary (summarizing the discussion and collaboratively developing a plan of action). A-L S-PIKES provides practical guidelines on how to prepare for and conduct these challenging conversations. It emphasizes effective communication tailored to the individual needs of PALS and their families, empathy, sensitivity, and support for PALS' emotional well-being and autonomy. The aim of A-L S-PIKES is to both enhance skills and confidence in delivering difficult news and to improve the QoL of PALS and their families. Future studies should systematically evaluate the feasibility and effectiveness of A-L S-PIKES to establish its utility in clinical practice.

Quantifying species biases among multidata sources on illegal wildlife trade and its implications for conservation.

Unsustainable wildlife consumption and illegal wildlife trade (IWT) threaten biodiversity worldwide. Although publicly accessible data sets are increasingly used to generate insights into IWT, little is known about their potential bias. We compared three typical and temporally corresponding data sets (4204 court verdicts, 926 seizure news reports, and 219 bird market surveys) on traded birds native to China and evaluated their possible species biases. Specifically, we evaluated bias and completeness of sampling for species richness, phylogeny, conservation status, spatial distribution, and life-history characteristics among the three data sets when determining patterns of illegal trade. Court verdicts contained the largest species richness. In bird market surveys and seizure news reports, phylogenetic clustering was greater than that in court verdicts, where songbird species (i.e., Passeriformes) were detected in higher proportions in market surveys. The seizure news data set contained the highest proportion of species of high conservation priority but the lowest species coverage. Across the country, all data sets consistently reported relatively high species richness in south and southwest regions, but markets revealed a northern geographic bias. The species composition in court verdicts and markets also exhibited distinct geographical patterns. There was significant ecological trait bias when we modeled whether a bird species is traded in the market. Our regression model suggested that species with small body masses, large geographical ranges, and a preference for anthropogenic habitats and those that are not nationally protected were more likely to be traded illegally. The species biases we found emphasize the need to know the constraints of each data set so that they can optimally inform strategies to combat IWT.

Cuantificación del sesgo por especies entre fuentes de datos múltiples para el mercado ilegal de fauna y lo que implica para la conservación Resumen El consumo insostenible y el comercio ilegal de fauna y flora silvestres amenazan la biodiversidad en todo el mundo. Aunque los conjuntos de datos de acceso público se utilizan cada vez más para obtener información sobre el mercado ilegal de especies silvestres, se sabe poco sobre su posible sesgo. Comparamos tres conjuntos de datos típicos con correspondencia temporal (4,204 sentencias judiciales, 926 informes de noticias sobre incautaciones y 219 encuestas sobre mercados de aves) de aves autóctonas de China objeto de comercio y evaluamos sus posibles sesgos por especie. En concreto, evaluamos el sesgo y la exhaustividad del muestreo de la riqueza de especies, la filogenia, el estado de conservación, la distribución espacial y las características del ciclo vital entre los tres conjuntos de datos a la hora de determinar los patrones del mercado ilegal. Las sentencias judiciales contenían la mayor riqueza de especies. En los estudios de mercado de aves y en los informes de noticias sobre incautaciones, la agrupación filogenética fue mayor que en las sentencias judiciales, donde las especies de aves canoras (Passeriformes) se detectaron en mayor proporción en los estudios de mercado. El conjunto de datos de noticias sobre decomisos contenía la mayor proporción de especies de alta prioridad para la conservación, pero la menor cobertura de especies. En todo el país, todos los conjuntos de datos informaron sistemáticamente de una riqueza de especies relativamente alta en las regiones sur y suroeste, pero los mercados revelaron un sesgo geográfico septentrional. La composición por especies en los veredictos judiciales y en los mercados también mostró patrones geográficos distintos. Hubo un sesgo significativo de rasgos ecológicos cuando modelamos si una especie de ave se comercializa en el mercado. Nuestro modelo de regresión sugería que las especies con masas corporales pequeñas, grandes áreas de distribución geográfica y preferencia por los hábitats antropogénicos y las especies que no están protegidas a nivel nacional tenían más probabilidades de ser objeto de comercio ilegal. Los sesgos de las especies que hallamos resaltan la necesidad de conocer las limitaciones de cada conjunto de datos para poder informar de manera óptima las estrategias de lucha contra el comercio ilegal de especies silvestres.

Fiction references as framing devices in extended reality news discourse.

This article examines fiction references in news coverage of extended reality. Based on a mixed methods analysis of 977 news articles from UK mainstream mass media outlets, this study found that fiction references were frequently used as framing devices within the news articles, with a focus on two franchises: The Matrix original trilogy (1999-2003) and Star Trek: The Next Generation (1987-1994). These references were utilised in the following three key ways: claiming fiction is becoming real; as a tool to improve readers' understanding of extended reality; and, to a limited degree, to create dystopic visions of extended reality. Ultimately, this article shows that, despite the dystopic representations of extended reality in fiction, fiction references have primarily been used to portray extended reality as advanced and high-quality. This supports extended reality adoption and the commercial interests of technology companies, raising questions as to whether journalists prioritise the interests of their readers when creating such news.

When assisted reproductive technology (ART) opens the door for inclusivity in a highly charged cultural milieu: an exploration of news articles on the establishment of ART clinics in Zimbabwe.

The study focuses on how infertility and assisted reproductive technology (ART) have been portrayed in the Zimbabwean print news media, specifically looking at articles related to the country's two private fertility clinics established in 2016 and 2017 respectively. Through thematic analysis of 35 news articles, seven prominent themes were developed: infertility as an undesirable and stigmatised condition; stress and the feminisation of infertility; the impact of societal and familial pressure to have children; ART as a ray of hope for infertile couples; growing acceptance of ART; availability, accessibility and affordability of ART; and the use of alternative medicines to cure infertility. The research highlights the coexistence of traditional medicine and ART in Zimbabwe, as well as the impact of stigma, pressure, and gender dynamics on infertile couples. Study findings signal how costly ART treatments may drive individuals towards potentially harmful traditional remedies. They also underscore the need for increased awareness of infertility, efforts to reduce stigma, and addressing barriers to ART access, particularly for men. Overall, findings shed light on the complexities surrounding infertility in Zimbabwe and the importance of addressing these issues in pursuit of better reproductive healthcare outcomes.

The effectiveness of a knowledge translation intervention on the implementation of NEWS2 in nursing homes, a pragmatic cluster RCT.

BACKGROUND: Improving the uptake of relevant and reliable research is an important priority in long-term care to achieve sustainable and high-quality services for the increasingly older population. AIM: The purpose was to assess the effectiveness of a tailored, adaptive and a multifaceted KT capacity program, relative to usual practice, on the implementation of National Early Warning Score 2 (NEWS2). METHODS: This study was carried out as a pragmatic cluster-randomized controlled trial. The capacity program consisted of an educational part to address implementation capacity gaps and a facilitation-upon-implementation part to address a relevant knowledge gap in nursing homes. A collective decision was made to address the challenge of early detection of clinical deterioration among nursing home residents, by implementing the (NEWS2) as clinical innovation. Public nursing homes in a Norwegian municipality (n = 21) with a total of 1 466 beds were eligible for inclusion. The study-period spanned over a 22-month period, including a 12-month follow-up. Data was extracted from the Electronic Patient Journal system and analyzed using multilevel growth model analysis. RESULTS: The intervention had a large effect on the use of NEWS2 among care staff in intervention nursing homes, compared to the control group (standardized mean difference, d = 2.42). During the final month of the implementation period, residents in the intervention group was assessed with NEWS2 1.44 times (95% CI: 1.23, 1.64) per month, which is almost four times more often than in the control group (mean = 0.38, 95% CI: 0.19, 0.57). During the follow-up period, the effect of the intervention was not only sustained in the intervention group but there was a substantial increase in the use of NEWS2 in both the intervention (mean = 1.75, 95% CI: 1.55, 1.96) and control groups (mean = 1.45, 95% CI: 1.27, 1.65). CONCLUSIONS: This tailored implementation strategy had a large effect on the use of NEWS2 among care staff, demonstrating that integrated knowledge translation strategies can be a promising strategy to achieve evidence-based care in the nursing home sector. TRIAL REGISTRATION: ISRCTN12437773 . Registered 19/3 2020, retrospectively.