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Food marketing has long been recognized to influence food preferences, consumption and health, yet little is known about the nature and extent of food marketing to young adults – especially with respect to their real-world encounters with food marketing and the appeals they find persuasive. This study aims to engage young adults to explore the persuasive power of food marketing and its platforms of exposure.

Participatory research with 45 young adults, who used a specially designed mobile app to capture the food marketing they encountered for seven days, including information on brand, product, platform and “power” (i.e. the specific techniques that made the advertisement persuasive).

A total of 618 ads were captured for analysis. Results revealed the dominance of digital platforms (especially Instagram, comprising 43% of ads), fast food and beverage brands (48% of ads) and the top persuasive techniques of visual style, special offer and theme.

This study uniquely draws from framing theory to advance the notions of selection and salience to understand food marketing power. It is the first study of its kind to provide a comprehensive look at the platforms and persuasive techniques of food marketing to adults as selected, captured and tagged by participants. It provides timely insights into young adults and food marketing to adults, including where it is encountered, the (generally unhealthy) brands and products promoted and how it is made meaningful.

As healthcare continues to become more expensive and complex, considering the voice of the patient in the design and operation of healthcare practices is important. Wound care and rural healthcare scenarios pose additional complexities for providers and patients. This study sought to identify key determinants of patient service quality in wound care.

Patients at the wound care/ostomy clinic (WOC) in a rural hospital were surveyed using the Kano model. The Kano model enables the categorization of quality attributes based on the attributes' contribution to the subject's overall satisfaction (and dissatisfaction). Chi-square goodness-of-fit testing, multinomial analysis and power analysis were then used to determine the Kano categories for each satisfaction-related attribute.

The analyses resulted in 14 one-dimensional attributes and 3 indeterminable attributes. For the one-dimensional attributes, customer satisfaction is directly proportional to the level of performance for that attribute. The one-dimensional attributes included providing correct care on the first, provision of necessary supplies for care, appropriately qualified medical staff and confidence in care provided by medical staff, among others. Understanding the attributes important to the patient drive patient-centered care, which improves positive patient outcomes and recovery. These attributes can then be used by healthcare professionals to design patient-centric processes and services. This research provides a framework for incorporating the voice of the patient into healthcare services.

While the research methodology can be used in other healthcare settings, the findings are not generalizable to other wound care clinics. This research was conducted in one small, rural hospital. In addition, the sample size was small due to the size of the wound clinic; therefore, an analysis of the differences between demographics could not be performed.

Considering the perspectives of rural wound care patients is important, as the patients are an under-served population with unique challenges related to patient care. The research findings detail rural patients' expectations during wound care treatments, which enable the clinic to focus on improving patient satisfaction. This research contributes to understanding the factors that are important to patient satisfaction in wound care. Further, the methodology presented can be applied to other healthcare settings.

While studies exist using the Kano model in healthcare and the literature is sparse in rural healthcare, this is the first case study using the Kano model in wound care to understand patient preferences.

The International Network of Research Management Societies (INORMS) celebrated its 20^th anniversary in 2021. It was established to increase communication among research management societies. The need for a formal international research management community developed because there was (1) increased international funding of research, (2) the number of international research collaborations was growing, and (3) there was a need to understand research regulations in other countries. INORMS sought to address these issues through international congresses and by providing a forum for member societies to work more closely together on common issues. Membership in INORMS steadily increased over the years. The 20^th anniversary meeting was highlighted with the signing of the Hiroshima Statement that described a research manager’s principles and responsibilities, which include collegiality, inclusiveness, professionalisation, innovation, and accountability. This chapter summarises the factors that led to the formation of INORMS and its history.

In this chapter, the author critically examines the deeply entrenched practices and theories within counselor education, revealing their roots in historically dominant, Eurocentric, and often racially oppressive assumptions. This study brings to light the pervasive impact of these traditional approaches, illuminating their role in perpetuating racial oppression and disparities in mental health care. The author presents a compelling argument for adopting Critical Race Theory (CRT) as an effective pedagogical and clinical practice framework in the counseling profession, a step toward its much-needed liberation. CRT's tenets are examined as a robust alternative, promoting socially just outcomes in counseling and psychotherapy. The article highlights CRT's capacity to address the well-established relationship between racism, white supremacy, and minority mental health. It proposes a groundbreaking model for praxis, predicated on CRT, which holds potential not only to challenge and disrupt oppressive structures but also to pave the way for the liberation of both the oppressed and the oppressor. This seminal work prompts a re-envisioning of counselor education, asserting a call for a transformative shift toward a liberation-based, social justice pedagogy.

This paper contributes to a dialogue about the psychoanalytic concept of free association and its application in the context of qualitative research interviewing. In doing so, it also adds to wider discussion regarding the relationship between clinical psychoanalysis, psychoanalytic psychotherapy and qualitative research.

Critical consideration of different perspectives on the application of free association in the qualitative research interview, extending earlier work addressing this issue. Differences and similarities in the way the concept of free association is articulated are examined regarding its framing in psychoanalysis and psychoanalytically oriented psychotherapy.

Whether researchers see themselves as borrowing, applying or drawing inspiration from free association, there is scope for muddling distinct ways of viewing it as it is conceived in psychoanalysis.

Considerations are outlined for researchers interested in psychoanalytically informed methods to be mindful of.

This study examined subjective numeracy and its relationship with accounting judgements on probability issues.

A subjective numeracy scale (SNS) questionnaire was distributed to 231 accounting students to measure self-evaluated numeracy. Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats.

The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. The findings suggest that switching the format of probability expressions from percentages to frequencies can improve the performance of participants with low self-evaluated numeracy.

Adding objective numeracy measurements could enhance results. Future numeracy research could add objective numeracy items and assess whether this influences participants' self-perceived numeracy. Based on this sample population of accounting students, the findings may not apply to large populations of accounting-information users.

Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy.

This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy. The authors also explored a method other than additional training to improve participants' performance on probability estimation tasks and discovered that frequency formats enhanced the performance of participants with low self-evaluated numeracy.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.