Parallel Process Model Research Articles

SIBLING RELATIONSHIPS AND COGNITIVE TRAJECTORIES IN THE WISCONSIN LONGITUDINAL STUDY

Abstract Sibling relationships are positively associated with psychological well-being over time. However, no prior studies have examined the impact of sibling relationships in middle and older adulthood on longitudinal patterns in cognitive functioning. Using four waves (spanning 27 years) of sibling data from the Wisconsin Longitudinal Study (N = 19,046), our study explores the relationship between sibling closeness, emotional support, and contact frequency in 1993, 2004, and 2011 (when participants were roughly 54, 65, and 72) and trajectories in cognitive measures including immediate and delayed recall, digit ordering, and letter fluency in later life (2004, 2011, and 2020). In growth curve models, sibling closeness (measured in 2004) and emotional support received from siblings (measured in 1993, 2004, 2011) were positively associated with baseline levels of letter fluency but not change over time. Next, we employed the sibling dyad data with at least one letter fluency data available from each source (n = 3,243 pairs of siblings) to fit a parallel process model to examine whether siblings’ letter fluency scores moved together over time. We found that both the intercepts and slopes of letter fluency trajectories were positively associated for siblings, implying that participants who experienced a steep decline in letter fluency were likely to have siblings who experienced similarly steep declines. Future studies can further explore what aspects of siblings relationship or shared exposures (e.g. shared family characteristics or environments, spending time together, having similar health behaviors, or providing care for each other) influence the shared cognitive patterns.

Do Incentives Mediate the Willingness of Civil Defence Force Volunteer Members to Respond in Pandemic and Stay as Volunteers?

During the COVID-19 pandemic, Malaysia opened over thirty Low-Risk COVID-19 Quarantine and Treatment Centre (PKRC). These PKRCs needed many volunteers to help first responders with non-medical services and patient management. Luckily many new volunteers signed up for this risky work. These volunteer hours were paid. How willing are they to volunteer? Will they stay? Did the incentive mediate volunteering and staying? To illuminate, this research was done. 391 respondents involved. They were the volunteers registered with the Civil Defence Force (the Force). They were chosen by voluntary sampling technique. An online survey was used for data collection. The survey instrument contains two main variables: willingness to respond and intention to stay, and a mediator: incentives. The instrument adapted the Expectancy Theory of Motivation and the Extended Parallel Process Model. The study found that their willingness to respond during pandemics was at a moderate level. Their intention to stay, however, is low. The availability of incentives has been identified as a significant mediator in influencing volunteers' willingness to respond during pandemics and retain them as Civil Defence Force volunteer members. This was significant because many people lost their jobs when the pandemic occurred. Their job alternative at that time was paid volunteering. Thus, it is relevant for the government to offer incentives if a huge number of volunteers is required during a long-term disaster.

The mediated amplification of societal risk and risk governance of artificial intelligence: technological risk frames on YouTube and their impact before and after ChatGPT

Over the past decade, many experts from academia, business, and civil society have warned of the risks of artificial intelligence (AI), such as large-scale job displacement, data bias against social groups, the use of AI to undermine democracy, the misuse of AI for mass surveillance and/or policing, the unregulated use of autonomous weapons, and others. However, research suggests that news media and social media representations emphasize the benefits over the risks of AI and rarely address issues of risk governance, while social media studies are still rare and largely based on Twitter/X data. This study proposes a theoretical framework of mediated risk communication based on the social amplification of risk framework (SARF), the extended parallel process model (EPPM), and framing. To understand prevalent AI-related risk frames and their role in mediated technological risk amplification/attenuation before and after the release of ChatGPT, a sample of the 501 most viewed YouTube videos about AI published between 2020 and 2023 were analyzed. Four frames with different emphases on societal threat and societal efficacy of AI were found with significant differences between risk/benefit emphases, AI evaluations, risk control/governance measures, and references to risk governance actors. Frames with higher emphasis on societal threat of AI were more likely to be viewed and commented on by YouTube users. AI-related threat frames increased significantly after the release of ChatGPT in 2022, suggesting its role as a risk-amplifying event. Implications for theory, future research, and AI risk governance are discussed.

The role of hope and fear in the impact of climate fiction on climate action intentions: Evidence from India and USA

There is a growing consensus that climate fiction might be an effective communication strategy to move the public on climate. However, empirical evidence documenting such an effect is limited, especially when it comes to climate fiction's potential to induce emotions of hope and fear, which are of key importance to the ongoing debate about the social effects of climate messages. To address this gap, we conducted an experimental cross-cultural study (N = 2268) with participants from India and the USA. In line with the Extended Parallel Process Model, we hypothesized that climate fiction combining fear and hope appeals (“ambitopian climate fiction”) would be more effective at stimulating climate action intentions than either fear-appealing (“dystopian”) climate fiction or hope-appealing (“utopian”) climate fiction. The hypothesis was not supported. We found that, in the US sample, dystopian climate fiction was more effective at stimulating climate action intentions than ambitopian climate fiction. However, ambitopian climate fiction was found to be efficient at inducing both hope and fear in both samples and at stimulating climate action intentions indirectly, in the Indian sample, through these emotions. The practical and theoretical implications of these findings are discussed.

Improving communication to increase uptake of high-risk breast cancer prevention appointments: Building a better letter

ObjectiveMailed letters to women identified as being at high-risk for developing breast cancer were not having the desired effect for encouraging appointments with prevention-focused providers at a large Midwest healthcare system. A partnership with communication scholars sought to revise the letter to increase awareness, intentions, and appointments. MethodsGuided by the Extended Parallel Process Model, survey responses were collected from letter recipients over the course of two years, both pre and post letter revision. Appointments attributed to letters were also tracked. ResultsRecipients of the revised letter had increased knowledge regarding the length of prevention appointments and indicated greater self-efficacy and intentions to make and attend appointments compared to those who received the non-revised letter. A greater percentage who received the revised letter also made appointments. ConclusionPartnering with communication scholars helped with improving a letter mailed to thousands of patients each year. Finding ways to increase response-efficacy of breast cancer prevention activities within communications may assist in increasing appointments. InnovationCross-disciplinary partnerships across the medical and social sciences – while not quick or simple - are essential for finding ways to improve patient wellbeing and hopefully reducing the prevalence of preventable diseases in the future.

An educational intervention based on extended parallel model to reduce exposure of pregnant women to BTEX air pollutants: A quasi-experimental study

Benzene, toluene, ethylbenzene, and xylene (BTEX) are air pollutants that may cause serious adverse effects on the health status of pregnant women and their fetuses. In this study, researchers aimed to investigate the effect of an educational intervention using an extended parallel process model on the protective behaviors of pregnant women against BTEX. In this quasi-experimental study, 94 first-trimester pregnant women were recruited for the study. The intervention caused significant increases in mean scores of perceived susceptibility, severity, response efficacy, and self-efficacy. The researchers found the effectiveness of educational intervention on protective behaviors to reduce exposure to BTEX air pollutants.

Sentiment analysis in infodemic management: leveraging the eppm Risk communication model

Infodemics are the rapid spread of false or misleading information related to public health emergencies, often through digital platforms. They can cause confusion, fear, and even harm to public health. This study investigates the application of sentiment analysis for infodemic management during disease outbreaks. Leveraging the Extended Parallel Process Model (EPPM) of risk communication, the research aims to categorize rumors based on their perceived threat level (high, medium, or low). Machine learning is employed to analyze infodemic text data collected from two Nigerian states (Oyo and Bauchi) to assess threat appraisal according to the EPPM model. The findings can inform targeted interventions for effective infodemic management before, during, and after outbreak of diseases.

Rethinking information disclosure to GenAI in hotels: An extended parallel process model

A critical, yet understudied aspect of generative AI functionality in hotels is consumers’ disclosure of personal information, which carries significant risks. By expanding the Extended Parallel Process Model, this research investigates the role of trust in hotels in impacting consumers’ perceptions of threat and coping efficacy. These perceptions, in turn, influence consumers’ fear perceptions and drive both adaptive behaviors (e.g., protective action and seeking help) and maladaptive behaviors (e.g., avoidance). Ultimately, the disclosure of personal information is strongly influenced by avoidance and, to some extent, by seeking help behaviors, but not by protective action behaviors. As the first study to examine consumers’ disclosure of personal information to generative AI, it extends the literature on processing arguments related to opaque systems like generative AI. Additionally, it provides insightful managerial implications, especially at a time when the hotel industry lacks clear guidance regarding the use of consumers’ personal information within generative AI.

Method of dynamic updating models of the interaction model of parallel processes in embedded systems

Method of dynamic updating models of the interaction model of parallel processes in embedded systems

Efficacy-focused and autonomy-supportive messaging in social media vaccine campaigns: insights from a Facebook split test

ABSTRACT Background In the context of social media vaccine campaigns, understanding the impact of message framing on audience engagement is crucial. This study examines the efficacy of messages centered on efficacy versus autonomy, informed by theoretical frameworks: the Extended Parallel Process Model (EPPM) and Self Determination Theory (SDT). Methods Utilizing the Facebook A/B Split Test (FBST) feature, an online experiment was conducted to compare the response to two different messages: one emphasizing vaccine efficacy (EPPM-informed) and the other focusing on individual autonomy in making vaccine decisions (SDT-informed). The primary measure of interest was the number of clicks on a link directing to a pro-vaccine website. Results The findings indicated that the autonomy-centered (SDT-informed) message resulted in a significantly higher click-through rate to the pro-vaccine website, particularly among users aged 65 and above. Furthermore, analysis of comments from Facebook users identified factors potentially contributing to vaccine hesitancy, with mistrust being the most dominant. Conclusions The study underscores the importance of tailoring social media vaccine campaigns to address the autonomy needs of the target audience while considering trust-related issues to mitigate vaccine hesitancy. Emphasizing individual autonomy in vaccine decision-making processes can enhance engagement with pro-vaccine content, especially in older demographics.

Women’s preferences for genetic screening in routine care: A qualitative study

ObjectiveExamine decision-making regarding when women would prefer to receive reproductive carrier and cancer predisposition screening and from what clinician. Methods20 women completed in-depth interviews via Zoom exploring their views on the provision of reproductive carrier and cancer predisposition screening. Our analysis identified themes related to what informs women’s preferences for when they would like to receive a genetic screening offer and by which clinician. ResultsParticipants’ responses to questions about when they would be interested in receiving genetic screening were best understood through the lens of the Extended Parallel Process Model. Specifically, personal utility of the information, a woman’s family health history and cost were key factors in decision-making. Women considered their clinician’s knowledge and their trust in and relationship with the clinician when deciding from whom they would prefer to receive an offer of genetic screening. ConclusionOB/GYN clinic patients may accept an offer of genetic screening from a knowledgeable and trusted clinician for carrier and cancer predisposition screening preconceptionally or prenatally. Practice implicationsIntegrating genetic reproductive and cancer predisposition screening into the care provided to reproductive age OB/GYN patients may be acceptable to this population.

A qualitative content analysis based on an extended parallel process model study of daycare center teacher behaviors concerning the eye health of preschool children

Purpose: This study is to explore the antecedent factors of daycare teacher behaviors concerning the eye health of preschool children by applying an extended parallel process model.Methods: Focus group interviews were conducted with ten daycare center teachers on September 4 and 14, 2023. A data analysis was performed according to the content analysis method by clustering the data into the four categories: the two threat factors of severity and susceptibility and the two efficacy factors of self-efficacy and response-efficacy.Results: Daycare center teachers’ perception of the severity of eye health problems in preschool children was high in relation to eye trauma, but it was recognized that viewing the electronic devices were of a less severe because symptoms were not noticed in a short period of time. They also showed low susceptibility because they were not sufficiently interested in the eye health hazard behaviors of preschool children. The self-efficacy of daycare center teachers was low because this was a lack of knowledge about symptoms of eye problems. However, they recognized that eye health activities performed in the preschool age could prevent negative eye health outcomes, thus showing a high response efficacy.Conclusion: In the future, it is necessary to increase the sensitivity and engagement of daycare center teachers concerning with the eye health of preschool children and to increase their self-efficacy. It will also be necessary to develop various interventions to improve eye health for preschool children that can be implemented by daycare center teachers.

Balancing news? Framing of threat and efficacy in leading Pakistani newspapers’ coverage of climate change

The study in hand quantitatively content analyzes the news frames in the leading Pakistani Urdu language and English language newspapers for 3 years. The study contributes to the theory of framing and the extended parallel process model. English language newspapers in comparison to Urdu language dailies in Pakistan had somewhat responsibly communicated the risk of climate change by covering both the effects of climate change and the climate efficacy.

Understanding invisible pain in the older adult: a content analysis of social media's representation of herpes zoster vaccine.

Herpes zoster (HZ), a common disease in older adults, affects their quality of life. Therefore, this study aimed to examine the blog posts of HZ-related information on social media platforms to analyze the attitudes and behaviors of residents toward the dissemination of health information. This research used content analysis to focus on Weibo, a representative social media in China, to analyze the content of 1866 blog posts related to herpes zoster (HZ) and herpes zoster vaccine (HZV). According to the consistency test by Cohen's Kappa, four themes were identified: (a) sources, (b) tones, (c) epidemiological information, and (d) extended parallel process model elements. The findings showed that most information on Weibo came from non-professionals, with a neutral tone, and showed the invisible pain of HZ and the effectiveness of HZV through the two largest aspects of prevention and aged protection in epidemiological information. However, current blog posts treat the older adult as invisible individuals, failing to acknowledge them as recipients of the information. Additionally, the cost of the vaccine acts as an invisible economic barrier, contributing to the dissemination of incorrect information about folk remedies. This impacts the older adult's acceptance of health information related to HZV. Thus, the way to share health information with the older adult needs to be improved in the future, and attention should be paid to the transmission of incorrect information to improve their vaccination rates and awareness of health management.

Explainable Spark-based PSO Clustering for Intrusion Detection

Given the exponential growth of available data in large networks, the existence of rapid, transparent and explainable intrusion detection systems has become of high necessity to effectively discover attacks in such huge networks. To deal with this challenge, we propose a novel explainable intrusion detection system based on Spark, Particle Swarm Optimization (PSO) clustering and eXplainable Artificial Intelligence (XAI) techniques. Spark is used as a parallel processing model for the effective processing of large-scale data, PSO is integrated for improving the quality of the intrusion detection system by avoiding sensitive initialization and premature convergence of the clustering algorithm and finally, XAI techniques are used to enhance interpretability and explainability of intrusion recommendations by providing both micro and macro explanations of detected intrusions. Experiments are conducted on several large collections of real datasets to show the effectiveness of the proposed intrusion detection system in terms of explainability, scalability and accuracy. The proposed system has shown high transparency in assisting security experts and decision-makers to understand and interpret attack behavior.

536-P: Characteristics of People with Type 2 Diabetes Based on the Extended Parallel Processing Model (EPPM)

536-P: Characteristics of People with Type 2 Diabetes Based on the Extended Parallel Processing Model (EPPM)

Parallel processing model for low-dose computed tomography image denoising

Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists’ ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN.

Managing Fear Responses: A Qualitative Analysis of Pictorial Warning Labels Five Years Post-Plain Packaging.

Although pictorial warning labels (PWLs) now dominate tobacco packages sold in many countries, few studies have probed how people who smoke respond to the threats presented several years post-plain packaging and larger PWLs. Understanding how people manage the fear and dissonance PWLs arouse, and the strategies they use to rationalize, diminish, and reject risk messages, could inform future PWL design. We undertook 27 in-depth interviews with people aged 18 and over (16 female, 8 Māori, and 13 aged ≤35) who smoked roll-your-own tobacco and lived in Aotearoa New Zealand. We probed participants' views on current PWLs and how they responded to these, then asked them to use alternative images and headlines to create new PWLs. We drew on the extended parallel processing model to interpret the data, which we analyzed using a reflexive thematic analysis approach. People who smoke dislike PWLs, which they think reduce them to diseased body parts. While a minority thought existing PWLs were believable and effective, most reported avoiding PWLs by hiding or cognitively blocking them. Participants used diverse counterarguments to diminish PWLs' relevance and impact, and a minority displayed strong reactance. Several suggested developing PWLs that recognized them as whole people rather than patients in waiting, and recommended greater use of testimonials, particularly from people who had successfully become smoke free. PWLs using more holistic and diverse messages could elicit greater engagement and responsiveness, and motivate cessation more effectively than existing health-oriented warnings. Our findings suggest existing PWLs, which aim to arouse fear of ill health, could be complemented by warnings that emphasize the benefits of quitting. Continuing to use threat-based PWLs could stimulate greater rationalization and reactance. By contrast, PWLs that aim to illustrate how cessation could benefit people who smoke and their families, rather than instill a fear of disease, could avoid message rejection and counter-argument, and may prove a more powerful way of motivating cessation.

A new communication approach to encourage lung cancer screening action in rural eligible populations

ObjectiveThe purpose of this study was to develop an effective communication approach to encourage lung cancer screening action within rural screening-eligible populations. MethodsAn iterative research approach using targeted engagement with the priority population was used. Findings were triangulated through multiple methods, including two surveys and concept testing interviews. The Health Belief Model and the Extended Parallel Process Model served as study frameworks. ResultsInitial findings suggest that threat levels are high in the priority population and an emphasis on barrier mitigation messaging may drive action. Health campaign posters integrating these findings were developed and tested with the priority population. The new health campaign posters were tested against examples of previously used health campaign posters. Findings suggest that the new health campaign posters were more effective in spurring lung cancer screening motivation and intention to act in the priority population compared to current health campaign poster examples. ConclusionMessaging focused on gain-framing, inoculation messaging, and barrier mitigation may be more effective in encouraging lung cancer screening action in rural eligible populations. InnovationThis project outlines a systematic process to developing effective, targeted communication approaches using behavior change and persuasive communication frameworks along with engagement from priority populations.

Examining the Role of Information Behavior in Linking Cancer Risk Perception and Cancer Worry to Cancer Fatalism in China: Cross-Sectional Survey Study.

Reducing cancer fatalism is essential because of its detrimental impact on cancer-related preventive behaviors. However, little is known about factors influencing individuals' cancer fatalism in China. With a general basis of the extended parallel process model, this study aims to examine how distinct cancer-related mental conditions (risk perception and worry) and different information behaviors (information seeking vs avoidance) become associated with cancer fatalism, with an additional assessment of the moderating effect of information usefulness. Data were drawn from the Health Information National Trends Survey in China, which was conducted in 2017 (N=2358). Structural equation modeling and bootstrapping methods were performed to test a moderated mediation model and hypothesized relationships. The results showed that cancer risk perception and cancer worry were positively associated with online health information seeking. In addition, cancer worry was positively related to cancer information avoidance. Moreover, online health information seeking was found to reduce cancer fatalism, while cancer information avoidance was positively associated with cancer fatalism. The results also indicated that the perceived usefulness of cancer information moderated this dual-mediation pathway. The national survey data indicate that cancer mental conditions should not be treated as homogeneous entities, given their varying functions and effects. Apart from disseminating useful cancer information to encourage individuals to adaptively cope with cancer threats, we advocate for health communication programs to reduce cancer information avoidance to alleviate fatalistic beliefs about cancer prevention.