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Flexibility is necessary in a dynamic healthcare environment. However, balancing flexibility and consistency is difficult for healthcare teams, especially when working in threatening conditions. Methods are needed to help teams create, monitor and maintain flexibility.

This study evaluates a practice-based program –– the Flexmonitor – which aims to help teams develop and maintain flexibility. Here, realistic evaluation was used to refine the program and define building blocks for future programs.

The Flexmonitor can be used to monitor implicit criteria and differences in interpretation and beliefs among team members to promote flexibility. It also monitors team behavior and the effects of this behavior on self-defined indicators. Using the Flexmonitor, team members can discuss their beliefs and the definitions and criteria of flexibility. Strikingly, teams were not able to effectively self-manage their flexibility using the Flexmonitor.

This article contributes to our knowledge of self-managing teams, particularly the question of whether team members can take responsibility for team flexibility.

Flexibility is essential for healthcare organizations to anticipate the increasing internal and external dynamics. Mental healthcare organizations in the Netherlands face major policy reforms made by the government, increasing involvement from municipalities and gradual replacement of clinical care with outpatient care. Top management plays an important strategic role in creating this flexibility because they make important choices, give direction and structure the organization. To create flexibility, managers have to deal with complexity and paradoxes. In this study, the authors aim to contribute to the knowledge on how healthcare managers can create flexibility in their organizations.

This is a qualitative empirical field study. In total, 21 managers of mental healthcare organizations participated in open in-depth interviews. The authors explored flexibility on three perspectives: organizational direction, structure and operations. The COVID-19 pandemic has provided an opportunity to explore flexibility. The authors asked participants to reflect on their organization's response to the pandemic.

Most mental healthcare organizations create flexibility in an implicit way. Flexibility and resilience are closely linked mechanisms. Flexibility ensures a quick response while resilience provides the counterforce and rebound needed to adapt. Adaption ensures that healthcare professionals learn from their experiences and do not return completely to the way things were done before. The primary urge to survive ensured rapid and adequate responses to the COVID-19 pandemic. Whether this is a manifestation of flexibility remains difficult to conclude.

The complexity theory offers some guidance in creating a flexible organization without losing consistency. Flexibility and resilience are closely linked mechanisms that antagonize and protect each other. With this insight, managers in mental healthcare can utilize the qualities and balance them without falling into the various pitfalls.

In this research, the authors are concerned with flexibility as a proactive attitude and capacity of organizations. By looking at the response of organizations to the COVID-19 crisis, the authors find out that responding to a disaster out of survival instinct is something else than flexibility. There is an interesting relationship between flexibility, resilience and adaptability, and they can balance each other.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

The purpose of this study is to explore the experiences of university students with nonconsensual pornography. The focus of the present work is on nonconsensual pornography – the nonconsensual distribution of intimate images and sexual extortion – that are becoming common experiences for many people. While the forms of nonconsensual pornography may vary, each case has one thing in common: the offender has shared a private image of the victim without the victim’s consent.

The data for this study was collected from student participants at a southeastern university. The stratified sample of university students was sent a link to an online survey and the responses of those who chose to respond were used in subsequent analyses (n = 300).

The findings of this exploratory study show low self-control as a significant predictor of sexting. Significant predictors of victimization via nonconsensual pornography included participation in sexting and use of dating apps.

While not generalizable, the descriptive data provide an important landscape for consideration of policy and legal recommendations to protect potential victims as well as would-be perpetrators beyond a university setting.

Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections.

In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns.

The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives.

To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.

The move toward e-health care in various countries is envisaged to reduce the cost of provision of health care, improve the quality of care and reduce medical errors. The most significant problem is the protection of patients’ data privacy. If the patients are reluctant or refuse to participate in health care system due to lack of privacy laws and regulations, the benefit of the full-fledged e-health care system cannot be materialized. The purpose of this paper is to investigate the available e-health data privacy protection laws and the perception of the people using the e-health care facilities.

The researchers used content analysis to analyze the availability and comprehensive nature of the laws and regulations. The researchers also used survey method. Participants in the study comprised of health care professionals (n=46) and health care users (n=187) who are based in the Dubai, United Arab Emirates. The researchers applied descriptive statistics mechanisms and correlational analysis to analyze the data in the survey.

The content analysis revealed that the available health data protection laws are limited in scope. The survey results, however, showed that the respondents felt that they could trust the e-health services systems offered in the UAE as the data collected is protected, the rights are not violated. The research also revealed that there was no significance difference between the nationality and the privacy data statements. All the nationality agreed that there is protection in place for the protection of e-health data. There was no significance difference between the demographic data sets and the many data protection principles.

The findings on the users’ perception could help to evaluate the success in realizing current strategies and an action plan of benchmarking could be introduced.

Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance.

Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML²S-SVM).

Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML²S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML²S-SVM, so it is necessary for users to identify proper parameters in advance.

On the basis of MTLS-SVM, a novel multi-label classification approach, ML²S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.