Social media and body image: Relationships between social media appearance preoccupation, self-objectification, and body image.

Nearly 85 % of emerging adults report using at least one social media site. Research suggests that viewing and internalizing unrealistic body ideals often displayed online may pose harmful effects on young people's body image. However, studies on the relationships between social media usage and body image have predominantly focused on women's drive for thinness. We sought to explore the relationships between social media appearance-related preoccupation (SMARP), body shame and surveillance, and drives for leanness, muscularity, and thinness, specifically examining the moderating role of gender within these relationships. Data from 939 undergraduate students (n = 240 men) were analyzed using multigroup structural equation modeling. Surveillance significantly mediated the positive associations between SMARP and drive for leanness for women and men. For SMARP and drive for muscularity, surveillance was a mediator for men only. Shame emerged as a significant mediator of the positive association between SMARP and drive for thinness for women and men. Moderated mediation was supported, such that the indirect effect of SMARP on drive for thinness was significantly stronger for women. These results suggest that for men in particular, SMARP is not necessarily associated with increased drives for leanness and muscularity unless men are also engaging in body surveillance.

Enrichment of rare codons at 5' ends of genes is a spandrel caused by evolutionary sequence turnover and does not improve translation.

Previously, Tuller et al. found that the first 30-50 codons of the genes of yeast and other eukaryotes are slightly enriched for rare codons. They argued that this slowed translation, and was adaptive because it queued ribosomes to prevent collisions. Today, the translational speeds of different codons are known, and indeed rare codons are translated slowly. We re-examined this 5' slow translation 'ramp.' We confirm that 5' regions are slightly enriched for rare codons; in addition, they are depleted for downstream Start codons (which are fast), with both effects contributing to slow 5' translation. However, we also find that the 5' (and 3') ends of yeast genes are poorly conserved in evolution, suggesting that they are unstable and turnover relatively rapidly. When a new 5' end forms de novo, it is likely to include codons that would otherwise be rare. Because evolution has had a relatively short time to select against these codons, 5' ends are typically slightly enriched for rare, slow codons. Opposite to the expectation of Tuller et al., we show by direct experiment that genes with slowly translated codons at the 5' end are expressed relatively poorly, and that substituting faster synonymous codons improves expression. Direct experiment shows that slow codons do not prevent downstream ribosome collisions. Further informatic studies suggest that for natural genes, slow 5' ends are correlated with poor gene expression, opposite to the expectation of Tuller et al. Thus, we conclude that slow 5' translation is a 'spandrel'--a non-adaptive consequence of something else, in this case, the turnover of 5' ends in evolution, and it does not improve translation.

Plastome evolution of Engelhardia facilitates phylogeny of Juglandaceae.

BACKGROUND: Engelhardia (Juglandaceae) is a genus of significant ecological and economic importance, prevalent in the tropics and subtropics of East Asia. Although previous efforts based on multiple molecular markers providing profound insights into species delimitation and phylogeography of Engelhardia, the maternal genome evolution and phylogeny of Engelhardia in Juglandaceae still need to be comprehensively evaluated. In this study, we sequenced plastomes from 14 samples of eight Engelhardia species and the outgroup Rhoiptelea chiliantha, and incorporated published data from 36 Juglandaceae and six outgroup species to test phylogenetic resolution. Moreover, comparative analyses of the plastomes were conducted to investigate the plastomes evolution of Engelhardia and the whole Juglandaceae family. RESULTS: The 13 Engelhardia plastomes were highly similar in genome size, gene content, and order. They exhibited a typical quadripartite structure, with lengths from 161,069 bp to 162,336 bp. Three mutation hotspot regions (TrnK-rps16, ndhF-rpl32, and ycf1) could be used as effective molecular markers for further phylogenetic analyses and species identification. Insertion and deletion (InDels) may be an important driving factor for the evolution of plastomes in Juglandoideae and Engelhardioideae. A total of ten codons were identified as the optimal codons in Juglandaceae. The mutation pressure mostly contributed to shaping codon usage. Seventy-eight protein-coding genes in Juglandaceae experienced relaxed purifying selection, only rpl22 and psaI genes showed positive selection (Ka/Ks > 1). Phylogenetic results fully supported Engelhardia as a monophyletic group including two sects and the division of Juglandaceae into three subfamilies. The Engelhardia originated in the Late Cretaceous and diversified in the Late Eocene, and Juglandaceae originated in the Early Cretaceous and differentiated in Middle Cretaceous. The phylogeny and divergence times didn't support rapid radiation occurred in the evolution history of Engelhardia. CONCLUSION: Our study fully supported the taxonomic treatment of at the section for Engelhardia species and three subfamilies for Juglandaceae and confirmed the power of phylogenetic resolution using plastome sequences. Moreover, our results also laid the foundation for further studying the course, tempo and mode of plastome evolution of Engelhardia and the whole Juglandaceae family.

Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective.

Free-floating bike sharing (FFBS) attracts increasing research focusing on usage patterns, determining factors, and integrated transportation. However, existing researchers tend to overlook the variation in usage characteristics over various time ranges, particularly the usage pattern at night. This paper is conducted to fill the gap through a series of analysis approaches on FFSB in Beijing. The characteristics of the usage pattern, including time-varying usage and traveling distance distributions, are initially illustrated. Subsequently, the spatial patterns of FFBS are visualized and thoroughly analyzed in different time ranges and origin-destination (O-D) flows. A statistical model evaluating the environmental effects of FFBS trips revealed the source of FFBS usage. In addition to focusing on the nighttime, the usage patterns varying day and night are compared through the analysis. The findings explain the usage pattern variation and the unique pattern at night, providing valuable insight for improving the management of the FFBS system.

Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM.

Background: Women's health WeChat public accounts play a crucial role in enhancing health literacy and fostering the development of healthy behaviors among women by disseminating women's health knowledge. Improving users' continuous usage behavior and retention rates for the women's health WeChat public account is vital for influencing the overall effectiveness of health communication on WeChat. Objective: This study aimed to construct a comprehensive model, delving into the key factors influencing women's continuance intention of the women's health public accounts from the perspectives of perceived health threats, individual abilities, and technological perceptions. The goal is to provide valuable insights for enhancing user stickiness and the effectiveness of health communication on WeChat public accounts. Method: An online survey was conducted among women receiving gynecological care at a certain hospital to gage their willingness for sustained use of the women's health WeChat public accounts. Through structural equation modeling, the study investigated the influencing factors on women's sustained intention to use the women's health WeChat public accounts. Results: The study included a total of 853 adult women. Among them, 241 (28.3%) women had followed women's health official accounts in the past but do not currently follow them, 240 (28.1%) women had followed women's health official accounts in the past and are still following them, and 372 (43.6%) women had never followed women's health official accounts. Currently, 240 women are still browsing women's health public accounts, 52 of whom read women's health public accounts every day, and most of them read women's health public accounts for 10-20 min at a time (100, 11.7%). The results of the structural equation model revealed that performance expectancy, social influence, hedonic motivation, habit, and e-health literacy had significantly positive effects on women's sustained intention to use public accounts (performance expectancy: ß = 0.341, p < 0.001; social influence: ß = 0.087, p = 0.047; hedonic motivation: ß = 0.119, p = 0.048; habit: ß = 0.102, p < 0.001; e-health literacy: ß = 0.158, p < 0.001). E-health literacy and self-efficacy indirectly influence sustained intention by affecting performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit. The effect sizes of e-health literacy on performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit were 0.244 (p < 0.001), 0.316 (p < 0.001), 0.188 (p < 0.001), 0.226(p < 0.001), 0.154 (p < 0.001), and 0.073 (p = 0.046). The effect sizes of self-efficacy on performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit were 0.502 (p < 0.001), 0.559 (p < 0.001), 0.454 (p < 0.001), 0.662 (p < 0.001), 0.707 (p < 0.001), and 0.682 (p < 0.001). Additionally, perceived severity and perceived susceptibility indirectly affected sustained intention by influencing performance expectancy and social influence. The effect sizes of perceived severity on performance expectancy and social influence were 0.223 (p < 0.001) and 0.146 (p < 0.001). The effect size of perceived susceptibility to social influence was 0.069 (p = 0.042). Conclusion: Users' e-health literacy, self-efficacy, perception of disease threat, and users' technological perceptions of the WeChat public accounts are critical factors influencing women's continuance intention of using the WeChat public accounts. Therefore, for female users, attention should be given to improving user experience and enhancing the professionalism and credibility of health information in public account design and promotion. Simultaneously, efforts should be made to strengthen users' health awareness and cultivate e-health literacy, ultimately promoting sustained attention and usage behavior among women toward health-focused public accounts.

Lumbar and Cervical Concurrent Muscle Activity Analysis During Hamstring Length Testing to Design a Customized Treatment Plan: An Observational Cross-Sectional Study.

BACKGROUND:  Hamstring length plays a significant role in a spectrum of clinical entities, from injury prevention and gait dysfunction to posture correction. Evidence suggests that the prevalence of hamstring tightness (HT)/reduced length is increasing. Despite the number of available tests and treatment protocols, HT is still a functional diagnosis. This study's primary goal is to evaluate concurrent muscle (CM) usage during these testing procedures to design a unique, customized treatment protocol. METHODS: The study was conducted in two stages. In phase 1, Active Straight Leg Raise (ASLR), Active Total Knee Extension (ATKE), and Active Seated Total Knee Extension (ASTKE) were carried out. Next, two pressure gauges (PGs) were placed to align with the natural lumbar and cervical curvatures while testing ASLR and ATKE. After analyzing the results for pressure gauge placement, phase 2 data were collected for tests ASLR and ATKE with PG. RESULTS: The results of ASLR and ATKE, both with and without PG, indicate a high prevalence rate, whereas the results of ASTKE show no prevalence. Changes in the PG values indicate CM usage. Dichotomization revealed that participants with normal test scores (non-HT group) had increased usage of CM work. Positive and negative changes in PG indicate the involved CM group. CONCLUSION(S): In regular practice, most healthcare professionals and fitness trainers prefer ASTKE due to the ease of the testing procedure. Directing co-professionals on their choice of tests is challenging, whereas providing knowledge about CM use paves the way for creating customized treatment plans.

Navigating challenges and opportunities: Nursing student's views on generative AI in higher education.

AIM: This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies. BACKGROUND: With the increasing prevalence of generative AI tools in academic settings, there is a growing interest in their use among students for learning and assessments. DESIGN: Employing a qualitative descriptive design, this study used semi-structured interviews with nursing students to capture the nuanced insights of the participants. METHODS: Semi-structured interviews were digitally recorded and then transcribed verbatim. The research team reviewed all the data independently and then convened to discuss and reach a consensus on the identified themes. RESULTS: This study was conducted within the discipline of nursing at a regional Australian university. Thirteen nursing students, from both first and second year of the programme, were interviewed as part of this study. Six distinct themes emerged from the data analysis, including the educational impact of AI tools, equitable learning environment, ethical considerations of AI use, technology integration, safe and practical utility and generational differences. CONCLUSIONS: This initial exploration sheds light on the diverse perspectives of nursing students concerning the incorporation of generative AI tools in their education. It underscores the potential for both positive contributions and challenges associated with the integration of generative AI in nursing education and practice.

Debate: How the business model of social media fuels the need for greater moderation.

The current business model of social media companies is to keep users engaged and on screen, so that data can be obtained from them, and targeted advertisements sold. To maintain user engagement, recommender systems (algorithms) promote content, which can be extreme, and includes mental health content. Extreme mental health content, such as self-harm images, can be pushed to any user and then more and more extreme content can be pushed to them. The normalisation, amplification or glamorisation of harmful content can have negative impacts upon the user, who may struggle to express what is happening online to anyone. In contrast with the views of Zhang et al. (Child and Adolescent Mental Health, 2024, 29, 104), without greater, timely moderation of harmful content, the promotion of self-harm, suicide and eating disorders will continue on social media, with potentially devastating consequences.

Antibiotic usage patterns in COVID-19 patients in five tertiary hospitals from Bangladesh: A countrywide picture.

Objectives: Irrational and injudicious use of antibiotics in COVID-19 patients could be detrimental in a tropical country with a weak antibiotic stewardship policy such as Bangladesh. This study aimed to focus on the antibiotic usage patterns in COVID-19 patients in Bangladesh. Methods: This prospective observational study was performed from July 2020 to June 2021 in five tertiary hospitals in Bangladesh. Data on demographic profile, disease severity, and antibiotic usage were collected directly from the patients' hospital documents. Results: A total of 3486 (94.4%) patients were treated with at least one antibiotic; 3261 (93.6%) patients received a single antibiotic, and 225 (6.5%) received multiple antibiotics. The most used antibiotics were ceftriaxone (37.3%), co-amoxiclav (26.3%), azithromycin (10.6%), and meropenem (10.3%). According to the World Health Organization AWaRe categorization, most (2260; 69.6%) of the antibiotics prescribed in this study belonged to the "Watch" group. Culture and sensitivity reports were available in 111 cases from one center. Only 18.9% of the patients were found to be co-infected with multi-drug-resistant bacteria (52.4% yield from sputum, 28.6% from urine, and 14.3% from blood). Conclusions: Strict antibiotic prescribing policy and antibiotic stewardship should be implemented immediately to limit the future threat of antimicrobial resistance in countries such as Bangladesh.

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.

BACKGROUND: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence-based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care. OBJECTIVE: This study is a process evaluation aiming to explore patients' engagement and experiences with the selfBACK app and specialist health care practitioners' views on adopting digital self-management tools in their clinical practice. METHODS: App usage analytics in the first 12 weeks were used to explore patients' engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis. RESULTS: Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance. CONCLUSIONS: Although the app's uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified. TRIAL REGISTRATION: ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043.

Sustainability initiatives in inpatient psychiatry: tackling food waste.

Background: Food plays a dual role in promoting human health and environmental sustainability. Yet, current food systems jeopardize both. Food waste poses a major global challenge due to its significant economic, social, and environmental impacts. Healthcare facilities generate the largest amounts of food waste compared to other forms of catering provision. Food waste correlates with environmental unsustainability and diminished patient satisfaction, compounding the prevalent challenge of hospital malnutrition and contributing to suboptimal patient outcomes. Materials and methods: In a three-year interventional study (2020-2022) at a psychiatric tertiary care center, we assessed and mitigated food waste using evidence-based measures. We conducted systematic food wastage audits over three years (2020-2022) in May and June, each lasting four weeks. Costs were analyzed comprehensively, covering food, staff, infrastructure, and disposal. Environmental impact was assessed using Umweltbelastungspunkte (UBP) and CO2e/kg emissions, alongside water usage (H2O - l/kg). Results: Economic losses due to food wastage were substantial, primarily from untouched plates and partially consumed dinners, prompting meal planning adjustments. Despite a >3% increase in meals served, both food waste mass and costs decreased by nearly 6%. Environmental impact indicators showed a reduction >20%. Vegetables, salad, and fruits constituted a significant portion of waste. Overproduction minimally contributed to waste, validating portion control efficacy. Conclusion: Our study highlights significant economic and environmental losses due to hospital food waste, emphasizing the importance of resource efficiency. The strategies outlined offer promising avenues for enhanced efficiency. The decrease in food waste observed over the three-year period underscores the potential for improvement.

Exploring the Chatbot usage intention-A mediating role of Chatbot initial trust.

Chatbots, an integral part of Digitalization, are becoming the cornerstone of modern-day marketing practices. This study's main objective is to identify the Chabot traits that have a bearing on users' confidence, acceptability, and engagement. Chatbots hold much potential for customer support in terms of service efficiency, cost savings, and customer experience. In addition to saving business costs, chatbots improve customer engagement, by reducing time and effort they need to spend in doing their business transactions. Aspiring, well-educated, and tech-savvy Gen Z customers perceive chatbots as highly compatible with their preferred method of seeking product-related information. As chatbots play a significant role in the marketing process, this study aims to identify the chatbot traits that influence customers' confidence, acceptance, and engagement with chatbots. Using the PLS-SEM tool/technique we computed a structural equation measurement model to test the traits leading to Chabot trust. The study also found that perceived ease of use, performance expectations, and social influence of chatbots are all greatly enhanced by trust on the part of the customers. Detailed statistical analysis showed that the trust that consumers have in chatbots strongly influences consumers' intentions, loyalty, and engagement levels. While gender had no impact on consumer intentions to use chatbots, the age of the respondents played a substantial role in their plans to use chatbots.

Codon usage bias in yeasts and its correlation with gene expression, growth temperature, and protein structure.

Codon usage bias (CUB) has been described in viruses, prokaryotes, and eukaryotes and has been linked to several cellular and environmental factors, such as the organism's growth temperature, gene expression levels, and regulation of protein synthesis and folding. Most of the studies in this area have been conducted in bacteria and higher eukaryotes, in some cases with different results. In this study, a comparative analysis of CUB in yeasts isolated from cold and template environments was performed in order to evaluate the correlation of CUB with yeast optimal temperature of growth (OTG), gene expression levels, cellular function, and structure of encoded proteins. Among the main findings, highly expressed ORFs tend to have a more similar CUB within and between yeasts, and a direct correlation between codons ending in C and expression level was generally found. A low correspondence between CUB and OTG was observed, with an inverse correlation for some codons ending in C. The clustering of yeasts based on their CUB partially aligns with their OTG, being more consistent for yeasts with lower OTG. In most yeasts, the abundance of preferred codons was generally lower at the 5' end of ORFs, higher in segments encoding beta strand, lower in segments encoding extracellular and transmembrane regions, and higher in "translation" and "energy metabolism" pathways, especially in highly expressed ORFs. Based on our findings, it is suggested that the abundance and distribution of preferred and non-preferred codons along mRNAs contribute to proper protein folding and functionality by regulating protein synthesis rates, becoming a more important factor under conditions that require faster protein synthesis in yeasts.

Repetitive Sequences, Codon Usage Bias and Phylogenetic Analysis of the Plastome of Miliusa glochidioides.

Annonaceae is the largest family in Magnoliales, exhibiting the greatest diversity among and within genera. In this study, we conducted an analysis of repetitive sequences and codon usage bias in the previously acquired plastome of Miliusa glochidioides. Using a concatenated dataset of shared genes, we constructed the phylogenetic relationships among 27 Annonaceae species. The results showed that the size of the plastomes in the Annonaceae ranged from 159 to 202 kb, with the size of the inverted repeat region ranging from 40 to 65 kb. Within the plastome of M. glochidioides, we identified 42 SSRs, 36 tandem repeats, and 9 dispersed repeats. These SSRs consist of three nucleotide types and eight motif types, with a preference for A/T bases, primarily located in the large single-copy regions and intergenic spacers. Tandem and dispersed repeat sequences were predominantly detected in the IR region. Through codon usage bias analysis, we identified 30 high-frequency codons and 11 optimal codons. The plastome of M. glochidioides demonstrated relatively weak codon usage bias, favoring codons with A/T endings, primarily influenced by natural selection. Phylogenetic analysis revealed that all four subfamilies formed monophyletic groups, with Cananga odorata (Ambavioideae) and Anaxagorea javanica (Anaxagoreoideae) successively nested outside Annonoideae + Malmeoideae. These findings improve our understanding of the plastome of M. glochidioides and provide additional insights for studying plastome evolution in Annonaceae.

Comparative proteomics uncovers low asparagine content in Plasmodium tRip-KO proteins.

tRNAs are not only essential for decoding the genetic code, but their abundance also has a strong impact on the rate of protein production, folding, and on the stability of the translated messenger RNAs. Plasmodium expresses a unique surface protein called tRip, involved in the import of exogenous tRNAs into the parasite. Comparative proteomic analysis of the blood stage of wild-type and tRip-KO variant of P. berghei parasites revealed that downregulated proteins in the mutant parasite are distinguished by a bias in their asparagine content. Furthermore, the demonstration of the possibility of charging host tRNAs with Plasmodium aminoacyl-tRNA synthetases led us to propose that imported host tRNAs participate in parasite protein synthesis. These results also suggest a novel mechanism of translational control in which import of host tRNAs emerge as regulators of gene expression in the Plasmodium developmental cycle and pathogenesis, by enabling the synthesis of asparagine-rich regulatory proteins that efficiently and selectively control the parasite infectivity.

Examining the growth in willingness to pay for digital wellbeing services on social media: A comparative analysis.

In recent years, there has been a growing need for social media platforms to offer services that preserve and promote users' digital wellbeing, including better protection of personal data and balanced technology usage. However, the current business model of social media is often seen as in conflict with users' digital wellbeing. In 2020, a study investigated users' willingness to pay monetary fees for social media digital wellbeing services. In the present work, we replicated this study in Q4 of 2022, aiming to explore any changes in interest and willingness to pay for these services. In addition, we extended the replication by conducting qualitative analysis on participants' comments to gain deeper insight and identify reasons for payment and reasons for rejecting to pay. Data were collected from 262 participants through an online questionnaire. The survey focused on four services: better data protection, less use of data for marketing, aiding users in controlling their prolonged usage, and reducing fake news and radicalisation on social media. The results showed that the willingness to pay for these services was significantly higher in 2022 compared to the results published in 2020. Participants expressed concerns about the feasibility and fairness of the alternative business model, which requires users to pay for safety and support. Our findings suggest a growing interest in digital wellbeing services, emphasizing the need for social media platforms to assess the feasibility of alternative business models, identify user segments, and take measures to enhance consumers' trust, accordingly.

Predictive modeling for identification of older adults with high utilization of health and social services.

AIM: Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions. METHODS: We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual's basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets. RESULTS: Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55-0.62 for the subgroups. CONCLUSIONS: Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data.

Virtual resonance: analyzing IPA usage intensity under COVID-19's isolating canopy.

The widespread adoption of smartphones coupled with advancements in artificial intelligence has significantly propelled the use of intelligent personal assistants (IPAs). These digital assistants have become indispensable for many users, particularly during the COVID-19 pandemic. Employing coviance-based structural equation modeling (CB-SEM) and analyzing data from 260 participants, this study explores the key factors influencing IPA usage intensity. Contrary to expectations, affective risk perception showed no significant impact on either IPA usage or parasocial interaction during the pandemic. In stark contrast, cabin fever syndrome significantly influenced both IPA usage and parasocial interaction, underscoring the role of environmental and psychological stressors in shaping technology use. Furthermore, loneliness was found to significantly enhance parasocial interaction with IPAs, though it did not affect usage intensity. The findings highlight a substantial connection between parasocial interaction and IPA usage intensity, suggesting that users who engage in human-like interactions with IPAs tend to use them more extensively. These insights not only deepen our understanding of how IPAs are utilized during health crises but also point to potential directions for developing IPAs that are more responsive to users' emotional and social needs.

Optimizing medication guidance support for patients with cancer pain: development and evaluation of a pharmaceutical care system for healthcare professionals.

BACKGROUND: Effective management of cancer pain critically depends on timely medication administration and adherence to precise medication guidelines. In the context of limited time and a busy healthcare environment, tailoring the optimal medication schedule for each patient with cancer pain presents a significant challenge for physicians and clinical pharmacists. METHODS: To address this challenge, we conducted a comprehensive analysis of healthcare professionals' needs in guiding cancer pain medication. By developing core features based on key user needs and continuously updating them, we have created the Universal Medication Schedule System (UMSS). We invited 20 physicians and pharmacists specializing in oncology or cancer pain to trial the system and assessed UMSS usage through distributed questionnaires. RESULTS: We identified five key needs of healthcare professionals in cancer pain medication guidance. Based on these needs, we (1) constructed a comprehensive drug information database, including basic information for 1135 drugs, 130,590 drug interaction data entries, and 1409 individual medication timing constraints, and (2) developed a web-based system that provides essential reference information such as drug interactions and dietary restrictions. It can create medication schedules and provide medication education tailored to the patient's daily routine. Participating evaluators unanimously agreed (100%) that the system aids in accurately assessing the risks of polypharmacy and quickly scheduling medication regimens. CONCLUSION: UMSS, by offering personalized medication schedule support, assists healthcare professionals in better managing patients' medication treatment plans. However, further improvements are needed in the automation of database updates and maintenance, as well as in integrating it with electronic health records.

[Can environmental impact be considered as a dimension of the proper use of medicines? From the 5th Forum of the Association for the Proper Use of Medicines]. / L'impact environnemental peut-il être considéré comme une dimension du bon usage des médicaments ? Retour sur le 5e Forum de l'Association Bon Usage du Médicament.

Through their footprint throughout their life cycle, from production to use, medicines have a significant impact on the environment. Reducing this impact is rarely considered from the perspective of the choices that healthcare professionals might have to make when prescribing or dispensing medicines. Should we consider environmental impact, alongside effectiveness and tolerance, one of the dimensions of the proper use of medicines? To address this question, the 5th Forum of the Association for the Proper Use of Medicines highlighted the main sources of pharmaceutical pollution: the carbon footprint linked to production, greenhouse gas emissions, the impact of residues on water and waste from packaging. While the eco-design of medicines should make it possible to limit their environmental impact upstream, there are still few initiatives aimed at their use. The Swedish "Hazard Score" assessment tool, which classifies compounds according to their potential to pollute the aquatic environment, was presented as a tool for guiding prescription choices. Through the exchanges between the various stakeholders (public authorities, doctors, pharmacists, manufacturers, patients) during this forum, recommendations were drawn up both on scientific and ethical grounds.