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Effectiveness of health promotion interventions in primary schools : a mixed methods literature review
Joca Zurc, Camilla Laaksonen, 2023, pregledni znanstveni članek

Opis: School-based health promotion interventions (HPIs) are commonly used in schools, but scientific evidence about the structures of effective interventions is lacking. Therefore, we conducted a mixed methods systematic literature review to recognize the HPI structures related to their effectiveness. Based on the inclusion criteria, 49 articles were selected for the literature review. The articles, published in 2011–2022, described 46 different school-based interventions conducted in 20 different countries. The average duration of the interventions was 12 months, and they were implemented mostly with an RCT study design (61.2%) and by targeting children (69.4%). Three main groups of interventions were identified and explained: (1) extensive and long-term interventions; (2) school policy-changing interventions; and (3) highly effective interventions. Effective school-based HPIs included multiple target groups, multiple providers with external experts, and an efficient duration and timing of follow-ups. The implications for educational research and school practice are presented. Evidence on the effectiveness of health-related interventions is still lacking and needs to be addressed in further studies.
Ključne besede: education, school health, health promotion, health education, intervention effectiveness, systematic literature review, mixed methods review
Objavljeno v DKUM: 21.03.2024; Ogledov: 91; Prenosov: 5
.pdf Celotno besedilo (935,20 KB)
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Wellness tourism research : bibliometric study of the scientific field
Milica Rančić Demir, Mila Zečević, 2022, pregledni znanstveni članek

Opis: The paper presents the results of a bibliometric analysis of academic research covering the field of wellness tourism. The purpose of the article is to identify the structure of relationships between topics that were current in the past and topics that are current now. The aim of the research was to better understand the development of the field of wellness tourism, important constructs, authors, and resources with the help of bibliometric methods. For detailed research of the field of literature on wellness tourism, some two set bibliometric strategies were used: co-citation and keyword co-occurrence. In our bibliometric mapping of the wellness tourism research field, we have investigated the scientific production of articles over the years, the most important journals in the field, and keyword co-occurrence patterns. The obtained results of the study showed a significant increase in the number of publications related to wellness tourism. When it comes to the importance of individual journals, we have identified the 20 best journals that have published the most articles in this field. In wellness tourism research, we identified four clusters: health tourism research, tourism research, wellness topics, and wellness tourism research.
Ključne besede: wellness tourism, bibliometrics, keyword co-occurrence, literature review
Objavljeno v DKUM: 22.02.2024; Ogledov: 77; Prenosov: 18
.pdf Celotno besedilo (221,32 KB)
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Text mining tourism literature
Ajda Pretnar Žagar, Tomaž Curk, 2021, objavljeni znanstveni prispevek na konferenci

Opis: Literature reviews are essential for understanding a specific domain as they map the main topics of current re-search. Our aim was to provide a framework for retrieving articles from online databases and analyzing them in a single script. We provide the analytical pipeline as open-source (https://github.com/tourism4-0/BibMine). The main research focus was on analyzing 318 abstracts from scientific papers on tourism and innovation, which we report in Zach et al. (2019). We used LDA topic modeling to uncover ten main topics, which we analyzed using pyLDAvis visualization. We used saliency and relevance scores to determine the main words that de-scribe a topic. The uncovered topics range from climate change and land use to smart destinations, travel expe-riences, and ICT. We performed similar analyses for the term "stakeholders," where we also observed the main verbs related to the query. Since verbs best define an activity, we used them to determine how stakeholders are involved in tourism development. Finally, we analyzed papers with the keyword "technology," where energy efficiency, VR, web technology, and augmented tourist experiences were the main topics.
Ključne besede: text mining, literature review, meta-analysis, topic modeling, tourism
Objavljeno v DKUM: 24.01.2024; Ogledov: 78; Prenosov: 2
.pdf Celotno besedilo (24,52 MB)
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Resident perceptions and responses to tourism : individual vs community level impacts
Tina Šegota, Tanja Mihalič, Richard R. Perdue, 2022, izvirni znanstveni članek

Opis: This paper reviews resident tourism attitude research through the lens of the individual- and community-level perceived impacts of and responses to tourism. It explores how perceived impacts of tourism and responses to tourism development have been conceptualised and measured in the existing resident attitudes models published between 1990 and 2020. Three categories of variables were identified and used: antecedent variables, tourism impact variables, and dependent variables. The latter three categories are used to discuss the research topic from the lenses of improvements in measurement instruments. Finally, the paper suggests rethinking the overall conceptualisation of residents’ perceptions of and reactions to tourism – it proposes future research directions to distinguish between individual-level and community-level effects and reactions.
Ključne besede: resident attitudes, support for tourism, tourism impact, personal benefit, host community, literature review
Objavljeno v DKUM: 18.01.2024; Ogledov: 107; Prenosov: 11
.pdf Celotno besedilo (2,21 MB)
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Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review
Lucija Gosak, Adrijana Svenšek, Mateja Lorber, Gregor Štiglic, 2023, pregledni znanstveni članek

Opis: Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.
Ključne besede: artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review
Objavljeno v DKUM: 27.11.2023; Ogledov: 269; Prenosov: 11
.pdf Celotno besedilo (654,91 KB)
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Violence against the elderly - analysis of further research needs based on a systematic review of research
Teja Primc, Branko Lobnikar, Kaja Prislan Mihelič, 2021, pregledni znanstveni članek

Opis: Purpose of the Paper: The purpose of this paper is to investigate the current state of research on the topic of elder abuse both in the institutional and domestic environment and to present the key findings of existing studies. The paper also aims to shed light on areas requiring additional research and present the directions for further research. Methods: A systematic literature review was conducted using the PRISMA method in the Web of Science and Scopus databases. The review included 29 papers published between 2010 and 2021. Findings: Elder abuse is an under-researched topic. The lack of research is particularly apparent in the incidence and nature of the problem in the home environment. Previous studies focus on the various forms of abuse, its perpetrators and risk factors. Rates of psychological abuse, neglect and physical abuse are high in nursing homes and among older visitors to ambulatory care facilities and emergency departments, while self-neglect, neglect and psychological abuse are prevalent in the home environment. Women between the ages of 70 and 75, who are socially isolated and require daily care, are more exposed to abuse. The perpetrators of the abuse are most often family members. Limitations/Applicability of the Research: The ‘paper’s findings provide insight into research on elder abuse and can serve as a basis for planning research into this phenomenon. The paper highlights research gaps and introduces opportunities for improvements in the systemic approaches when addressing the issue. Originality/Relevance of the Paper: The paper presents the systematic and up to date analysis of past studies on elder abuse. The results are intended for professionals and researchers working in the field.
Ključne besede: violence, abuse, neglect, elderly, systematic literature review
Objavljeno v DKUM: 26.01.2022; Ogledov: 826; Prenosov: 26
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Effectiveness of proactive password checker based on Markov models : doktorska disertacija
Viktor Taneski, 2019, doktorska disertacija

Opis: In this doctoral dissertation we focus on the most common method of authentication, the username-password combination. The reason for the frequent use of this authentication mechanism is its simplicity and low cost of implementation. Although passwords are so useful, they have many problems. Morris and Thompson, for the first time almost four decades ago, found that textual passwords were a weak security point of information systems. They have come to the conclusion that users are one of the biggest threats to information system’s security. Since then, we face these problems on a daily basis. Users do not perform the behaviours they need to be done in order to stay safe and secure, although they are aware of the security issues. Because this is a research area that security experts have been dealing with for a long time, in this dissertation we wanted to identify problems related to textual passwords and possible suggested solutions. For this purpose, we first performed a systematic literature review on textual passwords and their security. In doing so, we wanted to evaluate the current status of passwords in terms of their strength, ways of managing passwords, and whether users are still the “weakest link”. We found that one of the less researched solutions is proactive password checking. A proactive password checker could filter out the passwords that are easy-to-guess and only let through the passwords that are harder to guess. In order for a proactive password checking to be more effective, it is necessary for the checker to be able to check the probability that a certain password will be selected by the user. For this purpose, the better password checkers usually use certain tools to calculate password probability i.e., password strength. To find out which method is most suitable for calculating password strength, we have looked at similar solutions throughout history. We have found that Markov models are one of the most common methods used for password strength estimation, although we may encounter some problems when using them, such as sparsity and over-fitting. By reviewing similar solutions, we found that Markov models are mostly trained on only one dataset. This could limit the performance of the model in terms of correctly identifying bad or very strong passwords. As training datasets are important in the development of Markov models, it is clear that they will have some effect in the final assessment of the password’s strength. What we explore in our dissertation, is the importance of this effect on the final password strength estimation. Mainly, we focus on exploring the effect of different but similar datasets on password strength estimation. For the purposes of our study, we analysed publicly available sets of “common passwords” and processed them regarding the frequency distribution of the letters contained in these passwords. We built different Markov models based on these datasets and frequency distribution. This helped us determine if one Markov model was sufficient or if several models were needed to effectively estimate password strength for a wide range of passwords. The results showed statistical differences between the models. In more detail, we found that: - different Markov models (trained on different databases) showed statistically different results when tested on the same dataset, - more diverse datasets are needed to be able to calculate the strength of as many passwords as possible, since one “universal” model, trained on one “universal” dataset is less effective at classifying passwords in different categories (i.e., weak, medium, strong), - different Markov models of 1st and 2nd order, in most cases, give no statistically different outputs, - overall, Markov models can be used as a basis for constructing a more effective password checker that uses multiple different and specific Markov models, which could be more effective if we want to cover a wider range of passwords.
Ključne besede: passwords, password analysis, password security, password problems, password strength, systematic literature review, Markov models
Objavljeno v DKUM: 13.01.2020; Ogledov: 1467; Prenosov: 229
.pdf Celotno besedilo (1,12 MB)

Business process model and notation : the current state of affairs
Mateja Kocbek, Gregor Jošt, Marjan Heričko, Gregor Polančič, 2015, izvirni znanstveni članek

Opis: With business process modelling, companies and organizations can gain explicit control over their processes. Currently, there are many notations in the area of business process modelling, where Business Process Model and Notation (BPMN) is denoted as the de facto standard. Aims: The aim of this research is to provide the state-of-the-art results addressing the acceptance of BPMN, while also examining the purposes of its usage. Furthermore, the advantages, disadvantages and other interests related to BPMN were also investigated. Method: To achieve these objectives, a Systematic Literature Review (SLR) and a semantic examination of articles% citations was conducted. Results: After completing SLR, out of a total of 852 articles, 31 were deemed relevant. The majority of the articles analyzed the notation and compared it with other modelling techniques. The remainder evaluated general aspects of the notation, e.g. history and versions of the standard, usage of the notation or tools. Conclusion: Our findings demonstrate that there are empirical insights about the level of BPMN acceptance. They suggest that BPMN is still widely perceived as the de facto standard in the process modelling domain and its usage is ever-increasing. However, many studies report that only a limited set of elements are commonly used and to this end, several extensions were proposed. The main purpose of BPMN remains the description of business processes.
Ključne besede: business process modeling, BPMN, acceptance, use, systematic literature review
Objavljeno v DKUM: 06.07.2017; Ogledov: 1844; Prenosov: 709
.pdf Celotno besedilo (841,57 KB)
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