Dataset Information

A study on the effectiveness of narrative image types, message framing, and psychological distance in enhancing young people's self-efficacy in marine garbage recycling.

ABSTRACT: Intentional or unintentional littering, especially among young people, poses a threat to the marine environment. It is, therefore, necessary to enhance awareness of marine environmental protection among youth groups. This study explored the interaction between visual image types (photograph vs. cartoon), message framing (positive vs. negative), and psychological distance (human vs. turtle). A three-way analysis of variance (ANOVA) and regression analysis were used to verify the hypotheses. The key findings of the research were as follows: (1) Among the eight information construction methods, cartoons combined with turtles and presented with positive outcomes were the best way to communicate, i.e., cartoons showing animals in a friendly and positive way were the most effective form of marine garbage recycling for young people, and (2) Young people's preference for information will promote their self-efficacy in marine garbage recycling. The implications of these findings for developing marine protection information communication education for youth groups are also proposed.

SUBMITTER: Chen M

PROVIDER: S-EPMC11336297 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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A study on the effectiveness of narrative image types, message framing, and psychological distance in enhancing young people's self-efficacy in marine garbage recycling.

Chen Mo M Chen Kexin K Qin Yang Y Zhu Yanfei Y

Heliyon 20240721 15

Intentional or unintentional littering, especially among young people, poses a threat to the marine environment. It is, therefore, necessary to enhance awareness of marine environmental protection among youth groups. This study explored the interaction between visual image types (photograph vs. cartoon), message framing (positive vs. negative), and psychological distance (human vs. turtle). A three-way analysis of variance (ANOVA) and regression analysis were used to verify the hypotheses. The k ...[more]

PMID: 39170548

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Project description:BackgroundWith the advancement of cervical cancer elimination strategies, promoting human papillomavirus (HPV) vaccination is essential to achieving this goal. The issue of how to structure and develop message content to promote HPV vaccination is a debatable issue.ObjectiveThe efficacy of gain-loss framing in vaccination contexts is disputed. Our study aimed to elucidate the consequences of message framing on attitudes, intentions, and behavioral tendencies toward HPV vaccination, with the objective of refining message framing strategies and their elements.MethodsThis systematic review adhered strictly to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline reporting standards to comprehensively retrieve, extract, and integrate data. We searched databases, including PubMed, Embase, Scopus, and Web of Science, for literature published from database construction to August 15, 2023. Literature screening, data extraction, and quality evaluation were performed by 2 researchers. Intervention studies published in English, conducted with populations with children eligible for HPV vaccination, and involving message framing were included. Attitudes, intentions, and behaviors served as outcome evaluation criteria.ResultsA total of 19 intervention studies were included. Gain-loss framing had no clear effect on vaccination attitudes nor intentions. Loss framing showed a weak advantage at improving HPV vaccination attitudes or intentions, but the evidence was not strong enough to draw definitive conclusions. The impact of gain-loss framing on HPV vaccination behaviors could not be determined due to the limited number of studies and the qualitative nature of the analysis.ConclusionsCombining gain-loss framing with other message framing approaches may be an effective way to enhance the effect of message framing. More high-quality message framing content and exploring alternative moderator or mediator variables are required to support the conclusion.Trial registrationCRD42023451612; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=451612.

Project description:Waste management handles all kinds of waste, including household, industrial, municipal, organic, biomedical, biological, and radioactive wastes. People still face challenges in proper disposal methods for different types of waste, including landfill-bound items, recyclable materials, and biodegradable waste. Inadequate waste management poses a significant and multifaceted global challenge. The conventional method of segregating waste is a time-consuming and ineffective method that wastes human power and money. To address this issue in real time, sophisticated and sustainable waste management systems need to be implemented. The latest advancements in computer vision and deep learning offer efficient solutions for effective recycling and waste management. Existing deep learning models exhibited various limitations, such as detection accuracy and computational inefficiency, particularly when dealing with objects of varying sizes and exhibiting high degrees of visual similarity. These limitations generate various challenges in effectively capturing and representing the nuanced features of visually similar objects. To address this problem, we proposed the stacking of an enhanced Swin Transformer, improved ConvNeXt, and a spatial attention mechanism. The enhanced Swin transformers incorporate two key components- hierarchical feature extraction and shifting window mechanism to extract the global features from the garbage images effectively. The shifting window mechanism extracts the most important features from various regions of the images to identify the objects. In contrast, the hierarchical feature extraction captures long-range dependencies within the image to effectively identify different types of garbage. The improved ConvNext block with optimized parameterization extracts the local features of the image. This enhanced feature extraction capability enables the model to effectively discern fine-grained details of individual garbage particles, such as shape, texture, and subtle variations in color and appearance, leading to more accurate classification results. When we evaluated the performance of the proposed model using the publicly available Garbage Classification dataset, it attained 98.97% accuracy, 98.42% Precision, and 98.61% Recall. Due to its lightweight and low computational time and power, the proposed model surpasses the existing state-of-the-art deep learning models.

Project description:We tested the plausibility of a cognitive-emotional model to understand the effects of messages framed in terms of gain, non-loss, non-gain, and loss, and related to the health consequences of red/processed meat consumption. A total of 544 Italian participants reported their attitude toward reduced red/processed meat consumption and intention to eat red/processed meat (time 1 questionnaire). One week later, participants were randomly assigned to four different message conditions: (a) gain messages focused on the positive health outcomes associated with low meat consumption; (b) non-loss messages focused on the avoided negative health outcomes associated with low meat consumption; (c) non-gain messages focused on the missed positive health outcomes associated with high meat consumption; (d) loss messages focused on the negative health outcomes associated with high meat consumption (message sending). After reading the messages, participants answered a series of questions regarding their emotional and cognitive reactions to the messages, their evaluation of the messages, and again their attitude and intention toward red/processed meat consumption (time 2 questionnaire). Comparing different multivariate linear models under the Bayesian approach, we selected the model with the highest plausibility conditioned to observed data. In this model, message-induced fear influenced systematic processing, which in turn positively influenced message evaluation and attitude, leading to reduced intention to consume red/processed meat. Vice versa, message-induced anger reduced systematic processing, which in turn negatively influenced message evaluation, and led to no effect on attitude and intention. The comparison among message conditions showed that gain and non-loss messages activated integrated emotional and cognitive processing of the health recommendation, while loss and non-gain messages mainly activated emotional shortcuts toward attitude and intention. Overall, these results advance our comprehension of the effects of message framing on receivers' attitudes and intentions.

Dataset Information

A study on the effectiveness of narrative image types, message framing, and psychological distance in enhancing young people's self-efficacy in marine garbage recycling.

Publications

A study on the effectiveness of narrative image types, message framing, and psychological distance in enhancing young people's self-efficacy in marine garbage recycling.

Similar Datasets

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