Hasil untuk "Nursing"

Laureen J Hayes, L. O'Brien-Pallas, C. Duffield et al.

R. Fernandez, H. Lord, E. Halcomb et al.

Background Pandemics and epidemics are public health emergencies that can result in substantial deaths and socio-economic disruption. Nurses play a key role in the public health response to such crises, delivering direct patient care and reducing the risk of exposure to the infectious disease. The experience of providing nursing care in this context has the potential to have significant short and long term consequences for individual nurses, society and the nursing profession. Objectives To synthesize and present the best available evidence on the experiences of nurses working in acute hospital settings during a pandemic. Design This review was conducted using the Joanna Briggs Institute methodology for systematic reviews. Data sources A structured search using CINAHL, MEDLINE, EMBASE, PubMed, Google Scholar, Cochrane Library, MedNar, ProQuest and Index to Theses was conducted. Review methods All studies describing nurses’ experiences were included regardless of methodology. Themes and narrative statements were extracted from included papers using the SUMARI data extraction tool from Joanna Briggs Institute. Results Thirteen qualitative studies were included in the review. The experiences of 348 nurses generated a total of 116 findings, which formed seven categories based on similarity of meaning. Three synthesized findings were generated from the categories: (i) Supportive nursing teams providing quality care; (ii) Acknowledging the physical and emotional impact; and (iii) Responsiveness of systematised organizational reaction. Conclusions Nurses are pivotal to the health care response to infectious disease pandemics and epidemics. This systematic review emphasises that nurses’ require Governments, policy makers and nursing groups to actively engage in supporting nurses, both during and following a pandemic or epidemic. Without this, nurses are likely to experience substantial psychological issues that can lead to burnout and loss from the nursing workforce.

Wantana Maneesriwongul, J. Dixon

Shuwen Lu, Mark E. Lewis, Jamol Pender

This paper studies a sequential decision-making problem in a two-stage queueing system modeled after operations in CVS MinuteClinics, where nurse practitioners (NPs) oversee patient care throughout the entire visit. All services are non-preemptive, and NPs cannot begin treating a new patient until the current patient has completed both stages of care. Following an initial diagnosis in the upstream phase, NPs must decide for low-acuity patients whether to proceed with treatment independently through immediate service, or to collaborate with a dedicated general physician (GP) via telemedicine. While collaboration typically improves service quality and is preferred by individual patients, it may introduce delays as the NP-patient pair waits for a GP to become available. This work explores the structural properties of optimal policies under different system parameters, with a focus on large initial upstream queues, revealing unconventional and complex policy behaviors. Leveraging these structural insights and supporting theoretical results, we design simple and effective heuristics that are computable in linear time and suitable for practical implementation. These heuristics are robust across the entire parameter space of interest, and offer clear, actionable guidance for NPs as system parameters vary. They also achieve near-optimal performance, averaging within 0.1% of the optimal, while commonly used benchmark policies are highly sensitive to parameter shifts and can incur costs more than 100% higher than optimal. The work provides applicable insights for decision-makers on improving policy robustness and effectiveness, as well as recommendations for stakeholders on the value of investing in telemedicine infrastructure. For instance, we identify scenarios where such investments may be either unnecessary or essential based on specific system parameters.

Saraf Krish, Cai Yiyu, Huang Li Hui

During surgeries, there is a risk of medical gauzes being left inside patients' bodies, leading to "Gossypiboma" in patients and can cause serious complications in patients and also lead to legal problems for hospitals from malpractice lawsuits and regulatory penalties. Diagnosis depends on imaging methods such as X-rays or CT scans, and the usual treatment involves surgical excision. Prevention methods, such as manual counts and RFID-integrated gauzes, aim to minimize gossypiboma risks. However, manual tallying of 100s of gauzes by nurses is time-consuming and diverts resources from patient care. In partnership with Singapore General Hospital (SGH) we have developed a new prevention method, an AI-based system for gauze counting in surgical settings. Utilizing real-time video surveillance and object recognition technology powered by YOLOv5, a Deep Learning model was designed to monitor gauzes on two designated trays labelled "In" and "Out". Gauzes are tracked from the "In" tray, prior to their use in the patient's body & in the "Out" tray post-use, ensuring accurate counting and verifying that no gauze remains inside the patient at the end of the surgery. We have trained it using numerous images from Operation Theatres & augmented it to satisfy all possible scenarios. This study has also addressed the shortcomings of previous project iterations. Previously, the project employed two models: one for human detection and another for gauze detection, trained on a total of 2800 images. Now we have an integrated model capable of identifying both humans and gauzes, using a training set of 11,000 images. This has led to improvements in accuracy and increased the frame rate from 8 FPS to 15 FPS now. Incorporating doctor's feedback, the system now also supports manual count adjustments, enhancing its reliability in actual surgeries.

Elahe Vedadi, David Barrett, Natalie Harris et al.

Recent work has demonstrated the promise of conversational AI systems for diagnostic dialogue. However, real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals. Furthermore, physicians commonly oversee other team members in such activities, including nurse practitioners (NPs) or physician assistants/associates (PAs). Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system. We propose guardrailed-AMIE (g-AMIE), a multi-agent system that performs history taking within guardrails, abstaining from individualized medical advice. Afterwards, g-AMIE conveys assessments to an overseeing primary care physician (PCP) in a clinician cockpit interface. The PCP provides oversight and retains accountability of the clinical decision. This effectively decouples oversight from intake and can thus happen asynchronously. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) of text consultations with asynchronous oversight, we compared g-AMIE to NPs/PAs or a group of PCPs under the same guardrails. Across 60 scenarios, g-AMIE outperformed both groups in performing high-quality intake, summarizing cases, and proposing diagnoses and management plans for the overseeing PCP to review. This resulted in higher quality composite decisions. PCP oversight of g-AMIE was also more time-efficient than standalone PCP consultations in prior work. While our study does not replicate existing clinical practices and likely underestimates clinicians' capabilities, our results demonstrate the promise of asynchronous oversight as a feasible paradigm for diagnostic AI systems to operate under expert human oversight for enhancing real-world care.

Chinmay K Lalgudi, Mark E Leone, Jaden V Clark et al.

The recent widespread adoption of drones for studying marine animals provides opportunities for deriving biological information from aerial imagery. The large scale of imagery data acquired from drones is well suited for machine learning (ML) analysis. Development of ML models for analyzing marine animal aerial imagery has followed the classical paradigm of training, testing, and deploying a new model for each dataset, requiring significant time, human effort, and ML expertise. We introduce Frame Level ALIgment and tRacking (FLAIR), which leverages the video understanding of Segment Anything Model 2 (SAM2) and the vision-language capabilities of Contrastive Language-Image Pre-training (CLIP). FLAIR takes a drone video as input and outputs segmentation masks of the species of interest across the video. Notably, FLAIR leverages a zero-shot approach, eliminating the need for labeled data, training a new model, or fine-tuning an existing model to generalize to other species. With a dataset of 18,000 drone images of Pacific nurse sharks, we trained state-of-the-art object detection models to compare against FLAIR. We show that FLAIR massively outperforms these object detectors and performs competitively against two human-in-the-loop methods for prompting SAM2, achieving a Dice score of 0.81. FLAIR readily generalizes to other shark species without additional human effort and can be combined with novel heuristics to automatically extract relevant information including length and tailbeat frequency. FLAIR has significant potential to accelerate aerial imagery analysis workflows, requiring markedly less human effort and expertise than traditional machine learning workflows, while achieving superior accuracy. By reducing the effort required for aerial imagery analysis, FLAIR allows scientists to spend more time interpreting results and deriving insights about marine ecosystems.

Lili Zhou, Ke Cheng, Linbin Chen et al.

BackgroundSuboptimal medication adherence remains a major cause of allograft failure after kidney transplantation. Previous studies have focused on isolated factors rather than integrated mechanisms. Based on the COM-B model, this study investigates the mediating roles of medication beliefs and regulatory emotional self-efficacy (RESE) between medication literacy, social support, and medication adherence.MethodsA cross-sectional survey included 351 kidney transplant recipients (KTRs) from a tertiary hospital in Changsha (April-July 2025). Participants completed a general information questionnaire, the Basel Assessment of Adherence to Immunosuppressive Medications Scale, the Chinese version of the RESE Scale, the Social Support Rating Scale, the Chinese Medication Literacy Scale, and the Beliefs about Medicines Questionnaire-Specific. Data were analyzed using SPSS and AMOS for descriptive, correlational, hierarchical regression, and mediation analyses (bootstrapping with 5000 samples).ResultsThe medication non-adherence rate in KTRs was 37.6%, primarily due to missed doses (33.3%). Medication literacy, social support, medication beliefs, and RESE were significantly correlated with adherence (p < 0.01). After controlling for demographic variables, these factors explained 47.2% of the variance in adherence. Path analysis showed that medication literacy (β= -0.219) and social support (β= -0.180) directly reduced non-adherence and also indirectly improved adherence through medication beliefs and RESE. Specifically, medication literacy had indirect effects via medication beliefs (β= -0.034, 11.6%) and RESE (β= -0.039, 13.4%); social support exerted indirect effects through medication beliefs (β= -0.113, 35.0%) and RESE (β= -0.030, 9.3%). All bootstrap 95% CIs excluded zero.ConclusionMedication adherence among KTRs remains suboptimal. Within the COM-B framework, this study confirms that medication literacy and social support not only directly affect adherence but also exert indirect effects through the dual mediating pathways of medication beliefs and RESE. These findings suggest that clinical interventions should adopt a multidimensional approach, focusing not only on enhancing medication knowledge and support systems but also specifically addressing patients’ medication beliefs and emotional self-efficacy. A multi-path synergistic strategy is recommended to optimize intervention effectiveness.

Yodang Yodang, Erna Rochmawati, Sarah Amalia et al.

Introduction: Advance care planning (ACP) facilitates patients’ preferences for future treatment. It has been associated with improved quality of end-of-life care. While several factors contribute to ACP implementation, little is known about the specific components involved. This scoping review aimed to identify and explore components of ACP, including knowledge, attitudes, values and beliefs, cultural and ethnic factors in patients with cancer and their families. Methods: The scoping review followed the updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis Scoping Review (PRISMA-ScR) 2020 checklist. A search was conducted in the PubMed, Scopus, and Science Direct databases until December 2023, and the selected articles were assessed using the Joanna Briggs Critical Appraisal Tool (MMAT). Results: This scoping review included 42 studies. The included studies were grouped by design: quantitative (n=12), qualitative (n=15), and mixed methods (n=15). Key ACP components identified included knowledge and perceptions (n=28), attitudes and behaviours (n=33), values and beliefs (n=26), and cultural and ethnic aspects (n=13). Conclusion: This review provides a summary of the crucial components including knowledge and perception, attitudes and behaviors, values and beliefs, and cultural and ethnic perspectives to consider when implementing ACP for patients with cancer.

Vivi Retno Intening, Ros Eva Simanungkalit, I Wayan Sudarta et al.

Background: Patient-centered care, or PCC, has been recognized as a crucial pillar supporting both patient safety and healthcare quality. PCC must be monitored and evaluated, and reporting or documentation must be used as a communication mechanism. To determine the effectiveness of health care implementation, an assessment system is put in place. Purpose: To evaluate the implementation of PCC at a type C private hospital. Methods: A descriptive analytical design was applied in this study with a population of 385 inpatients. In this study, 97 respondents or 25% of the population participated as samples, chosen through purposive sampling. The instrument used was a questionnaire containing eight PCC indicators, namely patient choice, communication of information and education, coordination of services, moral support, physical comfort, involvement of family and loved ones, continuity and transition, and access to services. Percentage formulas was used in this study. This research has been declared ethically approved with document number 1117.1/RSPWDC/LP/KEPK/VIII/2023. Results: The dimensions of choice appreciation, moral support, physical comfort, continuity and transition, coordination, and integrated patients included in the good category as many as 97 respondents (100%), dimensions of family involvement and the closest people to patients included in the good category as many as 94 respondents (96.9%), and the dimensions of communication, information, and patient education included in the good category as many as 72 respondents (74.2%). Conclusion: The evaluation of the implementation of PCC implementation at a type C private hospital is in the good category.

Eri Otaka, Aiko Osawa, Kenji Kato et al.

Abstract BackgroundInterventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. ObjectiveThis study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. MethodsThis pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for “happy,” “sad,” “angry,” “surprised,” “scared,” and “disgusted” were calculated continuously using facial expression analysis with FaceReader ResultsA total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for “happy” was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; PP ConclusionsBy quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care.

Aweke A. Mitku, Temesgen Zewotir, Delia North et al.

Abstract Background Air pollution and several prenatal factors, such as socio-demographic, behavioural, physical activity and clinical factors influence adverse birth outcomes. The study aimed to investigate the impact of ambient air pollution exposure during pregnancy adjusting prenatal risk factors on adverse birth outcomes among pregnant women in MACE birth cohort. Methods Data for the study was obtained from the Mother and Child in the Environment (MACE) birth cohort study in Durban, South Africa from 2013 to 2017. Land use regression models were used to determine household level prenatal exposure to PM2.5, SO2 and NOx. Six hundred and fifty-six births of pregnant females were selected from public sector antenatal clinics in low socio-economic neighbourhoods. We employed a Generalised Structural Equation Model with a complementary log–log-link specification. Results After adjustment for potential prenatal factors, the results indicated that exposure to PM2.5 was found to have both significant direct and indirect effects on the risk of all adverse birth outcomes. Similarly, an increased level of maternal exposure to SO2 during pregnancy was associated with an increased probability of being small for gestational age. Moreover, preterm birth act a mediating role in the relationship of exposure to PM2.5, and SO2 with low birthweight and SGA. Conclusions Prenatal exposure to PM2.5 and SO2 pollution adversely affected birth outcomes after controlling for other prenatal risk factors. This suggests that local government officials have a responsibility for better control of air pollution and health care providers need to advise pregnant females about the risks of air pollution during pregnancy.

Kyler Larsen

As advancements in technology and medicine are being made, many countries are still unable to access quality medical care due to cost and lack of qualified medical personnel. This discrepancy in healthcare has caused many preventable deaths, either due to lack of detection or lack of care. One of the most prevalent diseases in the world is pneumonia, an infection of the lungs that killed 2.56 million people worldwide in 2017. In this same year, the United States recorded a pneumonia death rate of 15.88 people per 100000 in population, while much of Sub-Saharan Africa, such as Chad and Guinea, experienced death rates of over 150 people per 100000. In sub-Saharan Africa, there is an extreme shortage of doctors and nurses, estimated to be around 2.4 million. The hypothesis being tested is that a deep learning model can receive input in the form of an x-ray and produce a diagnosis with the equivalent accuracy of a physician, compared to a prediagnosed image. The model used in this project is a modified convolutional neural network. The model was trained on a set of 2000 x-ray images that have predetermined normal and abnormal lung findings, and then tested on a set of 400 images that contains evenly split images of pneumonia and healthy lungs. For each computer-run test, data was collected on a base measurement of accuracy, as well as more specific metrics such as specificity and sensitivity. Results show that the algorithm tested was able to accurately identify abnormal lung findings an average of 82.5% of the time. The model achieved a maximum specificity of 98.5% and a maximum sensitivity of 90% separately, and the highest simultaneous values of these two metrics was a sensitivity of 90% and a specificity of 78.5%. This research can be further improved by testing other deep learning models as well as machine learning models to improve the metric scores and chance of correct diagnoses.

SU Xiaolin, ZHANG Wenli, JIA Yanhuan et al.

ObjectiveTo explore the status quo and influencing factors of discharge readiness in patients with schizophrenia.MethodsA total of 140 patients with schizophrenia hospitalized in 4 tertiary grade A hospitals in Taiyuan from December 2021 to April 2022 were conveniently selected and surveyed by a general information questionnaire，Social Support Rating Scale（SSRS），and Readiness for Hospital Discharge Scale（RHDS）.ResultsThe total score of SSRS was（33.390±6.651） points，and the total score of RHDS was （99.630±7.585） points.Pearson correlation analysis showed that the scores of SSRS and RHDS were positively correlated（<italic>r</italic>=0.543，<italic>P</italic>&lt;0.001）.Multiple linear regression analysis showed that residence，education，course of the disease，and social support were the influencing factors of discharge readiness of schizophrenia patients，explaining 54.7% of the total variance.ConclusionThe discharge readiness of schizophrenic patients is generally at a high level.Medical staff should strengthen the education of patients with rural residence，low education background and short course of disease，providing them guidance to actively seek social support in order to further improve their readiness for discharge.but the education of patients with rural residence，low education background and short course of disease should be strengthened，so as to guide them to actively seek social support and further improve their readiness for discharge

Mi Hyang Choi, Misoon Lee

ObjectiveThis review aimed to evaluate the effectiveness of psychosocial and psychological interventions in nurses among intensive care units caring for pediatric patients.MethodsA literature search was performed in PubMed, EMBASE, CINAHL databases, using preferred reporting items for systematic reviews and meta-analysis guidelines. Study design, population characteristics, contents of the programs, measured outcomes, and results were systematically reviewed from 8 selected studies. To estimate the effect size, a meta-analysis of the studies was performed using the RevMan 5.3 program. The effect size used was the standardized mean difference.ResultsOf 1,630 studies identified, 4 met the inclusion criteria, and 3 studies were used to estimate the effect size of psychosocial and psychological interventions. The primary outcome variable of these studies was stress. The effect of the intervention program on stress was also found to have no effect in individual studies, and the overall effect size was not statistically significant (standardized mean difference = −0.06; 95% confidence interval: −0.33, 0.20; Z = 0.48, p = 0.630). However, according to the individual literature included in this study, after the stress management program was applied as a group, a significant stress reduction was shown in the experimental group (p = 0.021).ConclusionsThese results show that psychosocial and psychological interventions were effective in stress management by a group approach. Therefore, it is necessary to develop psychosocial support interventions for stress management of nurses among intensive care units caring for pediatric patients more diversely.

Syahrun Syahrun, Alfrina Hany, Masruroh Rahayu

Background: Dysphagia often occurs in post-stroke patients, causing aspiration that can result in disability or death. Nurses have an essential role to play in preventing these complications as they 24/7 care for patients. However, there is no written standard of nursing care regarding specific interventions of post-stroke dysphagia in reality. Objective: The purpose of this article is to conduct a literature review of interventions that can be made in patients with post-stroke dysphagia so that it can be a recommendation for Indonesian nursing standards. Design: Electronic literature searches PubMed, EBSCO (Medline), ProQuest, and ScienceDirect databases from January 2011 to October 2020. There was sixteen studies reviewed included in this systematic study were experimental, randomized controlled trials, or systematic reviews (which are also experimental designs, randomized controlled trials). The study focused on non-invasive interventions performed on post-stroke dysphagia patients. Results: Interventions in dysphagia found, namely: The use of food thickeners against the risk of aspiration resulted in the patient's swallowing ability significantly increased by 71.9% (p <0.01); Chin down intervention combined with thickening fluid provides a solution to improve the nutritional needs of patients dysphagia post-stroke; Tongue training interventions, swallowing training and speech therapy; Tongue stretching exercises that have a positive effect on tongue motility and oromotor function in post-stroke dysphagia patients; Intervention of Tongue resistance training that increases the strength of the tongue and reduces fluid residue in the vallecular; and early screening of dysphagia by nurses using formal guidelines to manage dysphagia patients thereby reducing chest infections and mortality. Conclusion: Nurses should not diagnose dysphagia, but can identify post-stroke dysphagia to determine the interventions necessary for nutrition management, hydration, and aspiration prevention. Interventions include early dysphagia screening within 24 hours after stroke, thickening nutrition according to nutritionist recommendations, laryngeal elevation exercises, peripheral stimulation, posture regulation, and education on eating and drinking.

Diana Vieira Brito, Carlos Gustavo Nunes da Silva, Livia Cristina Neves Rêgo et al.

Abstract Social organization in highly eusocial bees relies upon two important processes: caste differentiation in female larvae, and age polyethism in adult workers. Juvenile Hormone (JH) is a key regulator of both processes. Here we investigated the expression of two genes involved in JH metabolism - mfe (biosynthesis) and jhe (degradation) - in the context of social organization in the stingless bee Melipona interrupta. We found evidence that the expression of mfe and jhe genes is related to changes in JH levels during late larval development, where caste determination occurs. Also, both mfe and jhe were upregulated when workers engage in intranidal tasks, but only jhe expression was downregulated at the transition from nursing to foraging activities. This relation is different than expected, considering recent reports of lower JH levels in foragers than nurses in the closely related species Melipona scutellaris. Our findings suggest that highly eusocial bees have different mechanisms to regulate JH and, thus, to maintain their level of social organization.

Maude Wagner, Francine Grodstein, Karen Leffondre et al.

Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses' Health Study (19,415 women) to investigate the association between BMI history (collected from midlife) and subsequent cognitive decline after age 70. In conclusion, this approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Philipp Wicke, Marianna M. Bolognesi

Doctors and nurses in these weeks are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. Arguably the discourse around the current epidemic will make use of war-related metaphors too,not only in public discourse and the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a corpus of 200k tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY literal frame covers a wider portion of the corpus, among the figurative framings WAR is the most frequently used, and thus arguably the most conventional one. However, we conclude, this frame is not apt to elaborate the discourse around many aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options, or a metaphor menu, may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and ideas during the current pandemic.

Izabela Fajfer-Gryz, Ilona Nowak-Kózka, Joanna Rudek