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Framed by nostalgia marketing, this research draws upon lessons from ParticipACTION, a Canadian non-profit health promotion organization, to examine one of their most well-known campaigns, Body Break with ParticipACTION, in order to assess the potential role for nostalgia-based marketing campaigns in sport participation across generational cohorts.

Exploratory sequential mixed methods involving two studies were completed on behalf of ParticipACTION, with the authors developing the research instruments and the collection of the data undertaken by research agencies. Study 1 was the secondary analysis of qualitative data from five focus groups with different demographic compositions that followed a common question guide. Study 2 was a secondary data analysis of a pan-Canadian online survey with a sample (n = 1,475) representative of the overall adult population that assessed awareness of, and attitudes toward, ParticipACTION, Body Break, physical activity and sport participation. Path analysis tested a proposed model that was based on previous research on attitudes, brand and loyalty. Further, multi-group path analyses were conducted to compare younger generations with older ones.

The results provide direction and understanding of the importance of nostalgia in marketing sport participation programs across generational cohorts. For instance, in the four parent-adult focus groups, unaided references as well as frequent and detailed comments regarding Body Break were observed. Similarly, Millennials reported that Body Break was memorable, Canadian and nostalgic, with a mix of positive and negative comments. The importance of nostalgia was supported sequentially via results from the national survey. For example, while 54.1% of the 40–54 age-group associated ParticipACTION positively with Body Break, so did 49.8% of the 25–39-year age group, most of whom were not born when the promotion ran. Further, brand resonance was found to explain 4% more variance in moderate-to-vigorous physical activity (MVPA), the proxy for sport participation, for younger people compared to older people.

Results provide direction to brands, properties and agencies around the use of nostalgia in sport marketing campaigns and sponsorship efforts. For brands seeking to sponsor sport properties to alter their image with potential consumers in a new market, associating with a sport property that many view as nostalgic could improve the impact of the campaign. On the sport property side, event managers and marketers should both identify existing assets that members or fans are nostalgic about, as well as consider building nostalgia into current and new properties they develop.

This research is valuable to the sport marketing and sponsorship literature through several contributions. First, the use of nostalgia marketing, and nostalgia in general, is novel in the sport marketing and sponsorship literature, with future research in nostalgia and sponsorship recommended. Second, the potential to adopt or adapt Body Break to other sport participation and physical activity properties is empirically supported. Finally, the finding that very effective promotions can have a long-lasting effect, both on those who experienced the campaigns as well as younger populations who only heard about it, is notable.

This theoretical paper introduces a conceptual framework for Personal Cultural Heritage Management (PCHM), derived from prior research on migrants' information practices. It elaborates on the literature background and the development of the PCHM framework, highlighting the role of personal information management (PIM) and personal collections in the creation, access and utilization of cultural heritage information.

The study describes and explains the construction of the PCHM framework as a structured and self-motivated approach to personal heritage and identity learning.

Following the theoretical background and assumptions, along with the presentation of the key building blocks, the paper describes the key components of the framework, outlines their definitions and provides examples.

Theoretically, PCHM extends the current literature by encapsulating processes and actions employed by individuals to manage personal collections for cultural identity purposes, thereby underscoring the critical role personal collections play in both preserving and communicating cultural heritage.

PCHM can guide the development of support systems and policies to enhance cultural continuity and integration, thus empowering individuals to navigate their cultural identities confidently.

The PCHM framework creates a unique intersection between PIM and cultural heritage, providing a new perspective for understanding the dynamic evolution and formation of cultural identity among migrants.

A systematic investigation of postgraduate leadership programs for health and/or human services offered by Australian higher education institutions was undertaken.

Quantitative analysis identified the core characteristics of the programs. A thematic analysis of the course learning outcomes was conducted and six major themes of disciplinary leadership and management knowledge; research and analytical skills; professional practice; communication and collaboration; creativity and innovation; and system knowledge are shared in this study.

The authors conclude that Australian universities have taken an evidence-based approach to leadership education.

More work might need to be undertaken to ensure leadership theories are incorporated into learning outcomes.

Despite significant progress in Iran's immunization programs, vaccine policymaking in the country still faces various challenges and shortcomings. To address these issues and ensure sustained progress toward achieving comprehensive vaccination policies, it is essential to identify the critical factors influencing vaccine policies in Iran. Our study aims to provide evidence-based insights that can inform the development of effective and equitable vaccine strategies, leading to a more sustainable and efficient approach to vaccination in the country.

This mixed-method study aimed to analyze the factors influencing the future of human vaccine policy using Cross Impact Analysis. Firstly, a scoping review was conducted to identify the factors affecting the future of human vaccine development. Secondly, a semi-structured interview was conducted with experts in this field to add more factors and confirm the identified factors within the Iranian context. Finally, a Cross-Impact Analysis (CIA) approach was applied to comprehend the complex relationships between the identified factors. Thematic analysis was used for the qualitative data, and MICMAC analysis was applied to characterize the relationships between the factors.

Seventeen key driving force factors were identified through comprehensive review and interviews. These factors were assigned weighted values ranging from zero to three and subsequently analyzed using MICMAC software. Employing the Cross-Impact Analysis (CIA) technique, the study characterized the impact of each factor on vaccine policy and elucidated the intricate interactions between them. The findings underscored that robust leadership and governance, an innovative ecosystem, and well-established immunization information systems emerged as pivotal driving forces shaping vaccine policy in Iran.

While this study contributes valuable insights into the driving factors influencing vaccine policy in Iran, it is important to acknowledge several limitations. The results rely on the subjective perceptions of a diverse group of specialists, and future research could delve into additional factors in other countries to identify common themes and differences.

This study provides evidence to assist policymakers in making informed decisions regarding vaccines in Iran. The findings suggest that enhancing access to vaccines, fostering trust in the healthcare system, and prioritizing equity in distribution can contribute to increased vaccination rates and a reduction in vaccine-preventable diseases.

This study provides a unique contribution to the field of vaccine policy by utilizing the cross-impact analysis to examine the complex interactions among various factors. The results of this analysis demonstrate that these interactions can significantly impact the overall system, highlighting the need for policymakers to consider multiple factors when formulating effective strategies. By revealing the significance of these interactions, this research offers valuable insights into the development of successful policies that can shape a desirable future for vaccine policy in Iran. Future studies could ratify the findings from this research by applying other methodological approaches.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.