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Research suggests that children in Hong Kong are at an elevated risk of emotional problems. Authoritarian parenting, a common parenting style in Hong Kong, is a critical factor associated with childhood mental health problems. The purpose of this paper is to evaluate the effectiveness of the 6As Positive Parenting Program (6As) in modifying parenting attitudes, reduction of parenting stress and increasing self-efficacy in positive parenting, among a sample of 82 Hong Kong parents. 6As focuses on prevention by instilling positive parenting beliefs and principles, thereby reducing reliance on authoritarian and related parenting styles.

The program was evaluated using a controlled pre-post-treatment design. Outcome measures were the Chinese Child-rearing Beliefs Questionnaire, Parental Stress Scale, Parenting Self-efficacy Scale and participant feedback. ANOVA and correlation were utilized to detect treatment effects and relationships between the degree of change among measures and subscales.

Relative to the control group, the 6As Positive Parenting Program significantly changed parents’ parenting attitudes, reduced parenting stress and increased self-efficacy in positive parenting. In sum, 97.6 percent of the participants agreed that the program is a good fit for the Hong Kong culture.

A larger sample would have been desirable for this study. One factor that limited analyzable data was that some of the participating organizations enrolled participants into the program who did not meet research inclusion criteria. Furthermore, the size of groups varied from 5 to 16 participants, which may have produced different group dynamics that added variability to outcomes. Future 6As parenting program research should attempt to standardize group size or to directly compare the effectiveness of smaller vs larger groups. Furthermore, as noted earlier, the research was conducted during the primary school application period, which may have heightened the parenting stress for parents of younger children.

The results suggest that an enhanced belief in authoritative, autonomy and training strategies could reduce parental stress and shift parents’ attitude toward a more positive approach in child-rearing.

There is considerable need for a holistically designed parenting training that is culturally credible and sensitive.

Findings suggest that positive parenting can enhance the parent–child relationship and reduce parental stress. The results support governmental, non-governmental organizational and community focus on positive practices for parenting training in Hong Kong.

The purpose of this paper is to explore differences in the degree of innovation in employment relations (ER) between emerging and established firms,

A large national telephone survey (N=1,416) of both emerging (<5 years) and established firms was conducted.

Emerging firms were more casualised, less unionised, and experiencing higher levels of market expansion and unpredictability. Despite these differences, younger firms showed otherwise remarkable similarity to older firms across a range of ER practices, and both categories showed a reliance on business networks, rather formal training, for ER knowledge. While introducing ER changes more rapidly than older (and larger) firms, they were converging towards a suite of ER practices similar to that adopted by older firms. The results suggest that, if anything, established firms may have been engaged in greater innovation in more unusual ER practices.

Only managers were surveyed. The data are cross-sectional rather than longitudinal. As the study was undertaken in only one country, replication in other settings would be desirable.

The results raise major doubts about the notion that new firms represent the cutting edge of innovation, and highlights the degree to which newer firms match or mimic older firms’ ER architecture.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The purpose of this paper is to analyse why Malta continues to show the highest level of turnout for European Parliament (EP) elections in a country where voting is not obligatory. By analysing the Maltese EP elections from 2004 to 2019, the paper seeks to understand why the Maltese engage with a second order election to the degree that they do.

The paper is a combination of qualitative and quantitative methods, first assessing the context of the 2019 elections, the historical trends and then the factors that help explain why the Maltese continue to engage with EP elections.

The paper finds that the Maltese political system, highly polarised and dominated by two parties, primarily galvanises people to engage with elections, that it is more about party leadership than actual engagement with Europe and that second order elections in Malta are often run as first order elections.

This paper is the only systematic evaluation of the 2019 EP elections in Malta, discusses categorically that EP elections are rarely about Europe while also showing clearly that political parties can make second-order elections appear as first-order elections should the stakes be high enough.

Personas are lifelike characters that are driven by potential or real users’ personal goals and experiences when interacting with a product. Personas support user-centered design by focusing on real users’ needs. However, the use of personas in educational research and design requires certain adjustments from its original use in human-computer interface design. This paper aims to propose a process of creating personas from phenomenographic studies, which helps us create data-grounded personas effectively.

Personas have features that can help address design problems in educational contexts. The authors compare the use of personas with other common methodologies in education research, including phenomenology and phenomenography. Then, this study presents a six-step process of building personas using phenomenographic study as follows: articulate a design problem, collect user data, assemble phenomenographic categories, build personas, check personas and solve the design problem using personas. The authors illustrate this process with two examples, including the redesign of a professional development website and an undergraduate research program design.

The authors find that personas are valuable tools for educational design websites and programs. Phenomenography can productively help educational designers and researchers build sets of personas following the process the authors propose.

The use and method of personas in educational contexts are scarce and vague. Using the example contexts, the authors provide educational designers and researchers a clear method of creating personas that are relatable and applicable for their design problems.

Educational design patterns offer practical strategies that can be shared and adapted to address problems in teaching and learning. This article explores how educational design patterns for connected learning at scale at an Australian university may be adapted to a Vietnamese higher education context.

12 educational design patterns that address the challenges of active learning and large teaching team management are discussed. The authors then critically reflect on their cross-cultural adaptation for the higher education context, from an Australian to a Vietnamese university.

Transitioning from passive to active learning strategies and effectively leading large teaching teams present similar challenges across our contexts. Educational design patterns, when dynamically adapted, may assist educators to teach skills that are critical for work and the future. Higher education institutions globally could enhance their practices by incorporating international best practice approaches to educational design.

The Connected Learning at Scale (CLaS) educational design patterns explored in this article offer solution-oriented strategies that promote a more active learning experience. This paper identifies adaptations for educators, especially those in Vietnamese higher education that respect traditional structures, cultural nuances and resource limitations in implementation.

Whilst educational design patterns are well-researched in the Western contexts, few studies analyse design patterns in an Asian, and in particular the Vietnamese context. More research is needed in the cross-cultural adaptation of educational design patterns that joins practice and theory.

The purpose of this study is to explore the contribution of what the authors have termed, “managed opportunities for independence” (MOI) in building the resilience of young people in care.

The study used a qualitative, grounded theory methodology. Nine child and youth care workers were purposively sampled from various child and youth and care centres in South Africa.

Findings indicate that MOI contribute to the development of resilience of young people in care.

Care-leaving literature recognizes that too much protection does not adequately prepare young people for independent living. There is also increasing attention to the resilience processes that enable care-leavers to thrive during the transition from care to independent living. However, there is limited empirical research that looks at how in-care programmes develop young people’s resilience. In addition, very little is said about what it means for child and youth care practice. This study’s focus on the contribution of “managed opportunities for independence” in building the resilience of young people in care provides a foundation for understanding the care-leaving process better.

This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

Research on workplace stress measurements varied without much accuracy and effectiveness. The purpose of this paper is to introduce a new quantitative assessment tool emWave Pro Plus (Institute of HeartMath) and compare heart rate variability (HRV) results with the Personal and Organizational Quality Assessment (POQA) and the Perceived Stress Scale (PSS).

This research opted for a correlational study which involves 85 full-time employees who were working at least 40 h per week in a large corporation participated in this study. The POQA and PSS were used to correlate with HRV.

Astonishing findings emerged in this study. Significant positive correlations were found between emotional stress and HRV, and between intention to quit and HRV. In other words, the researchers have to make sense the following surprising findings: the higher the emotional stress an employee faces, the healthier they are. Healthier employees may have higher intentions of quitting their jobs. The surprising results may be attributed to personality, culture, emotional regulation and age among others.

This research fulfills an identified need to validate quantifiable stress measurements especially in a corporate environment. The research also shows promising results, and future studies should continue to tap into HRV as an objective measure of mental health and workplace stress.