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OBJECTIVES: Dental graduates must graduate with high levels of clinical skills. Education in the clinical environment needs to be more than didactic supervision of practice by clinical teachers. Appropriate feedback in this context, is therefore critical to the development of student competence and confidence. This study was conducted to enhance and develop the assessment and feedback processes during clinical sessions in a Dental University Hospital in an effort to contribute to the development of students' self-assessment skills, reflective ability and clinical competence. METHODS: A new evidence-based model of feedback was introduced between clinical teachers and dental students. The implementation of this model was evaluated by students through a survey and focus groups. Descriptive and inferential statistics were applied to the quantitative data, while thematic analysis applied to the qualitative data. RESULTS: Findings from the survey indicated that students perceived the new model of feedback to be a positive addition to their learning experiences. The majority indicated a preference to continue using it. Quantitative analysis also demonstrated that students placed a high value on the feedback they received through the new model and associated it with improved individual performance. Five themes generated from the qualitative analysis echoed the perception that the model of feedback enhanced learning opportunities, especially when it was focused on individual performance and incorporated peer feedback. Students' preferences in relation to feedback processes were also gleamed from quantitative and qualitative analyses, that is, provision of positive and constructive feedback, both in dialogue and in written formats, delivered during and after each clinical session and addressing their individual competency learning goals for the future. Some challenges to be addressed were also identified (e.g., time constraints, inter-personal issues, and non-conducive environments). CONCLUSIONS: Feedback is central to learning and remains a complex and challenging area. By adopting effective and evidence-based feedback practices through the introduction of a feedback model, students can be supported in regulating their own learning in the clinical learning environment.

The British Association for Psychopharmacology guidelines specify the scope and targets of treatment for bipolar disorder. The third version is based explicitly on the available evidence and presented, like previous Clinical Practice Guidelines, as recommendations to aid clinical decision making for practitioners: it may also serve as a source of information for patients and carers, and assist audit. The recommendations are presented together with a more detailed review of the corresponding evidence. A consensus meeting, involving experts in bipolar disorder and its treatment, reviewed key areas and considered the strength of evidence and clinical implications. The guidelines were drawn up after extensive feedback from these participants. The best evidence from randomized controlled trials and, where available, observational studies employing quasi-experimental designs was used to evaluate treatment options. The strength of recommendations has been described using the GRADE approach. The guidelines cover the diagnosis of bipolar disorder, clinical management, and strategies for the use of medicines in short-term treatment of episodes, relapse prevention and stopping treatment. The use of medication is integrated with a coherent approach to psychoeducation and behaviour change.

BACKGROUND: Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base. METHODS: We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder. RESULTS: We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents. CONCLUSIONS: Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.

OBJECTIVE: To identify features of clinical decision support systems critical for improving clinical practice. DESIGN: Systematic review of randomised controlled trials. DATA SOURCES: Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews. STUDY SELECTION: Studies had to evaluate the ability of decision support systems to improve clinical practice. DATA EXTRACTION: Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature. RESULTS: Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations. CONCLUSIONS: Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.

Over the past decade, social media platforms have penetrated deeply into the mech­anics of everyday life, affecting people's informal interactions, as well as institutional structures and professional routines. Far from being neutral platforms for everyone, social media have changed the conditions and rules of social interaction. In this article, we examine the intricate dynamic between social media platforms, mass media, users, and social institutions by calling attention to social media logic—the norms, strategies, mechanisms, and economies—underpin­ning its dynamics. This logic will be considered in light of what has been identified as mass me­dia logic, which has helped spread the media's powerful discourse outside its institutional boundaries. Theorizing social media logic, we identify four grounding principles—programmabil­ity, popularity, connectivity, and datafication—and argue that these principles become increas­ingly entangled with mass media logic. The logic of social media, rooted in these grounding principles and strategies, is gradually invading all areas of public life. Besides print news and broadcasting, it also affects law and order, social activism, politics, and so forth. Therefore, its sustaining logic and widespread dissemination deserve to be scrutinized in detail in order to better understand its impact in various domains. Concentrating on the tactics and strategies at work in social media logic, we reassess the constellation of power relationships in which social practices unfold, raising questions such as: How does social media logic modify or enhance ex­isting mass media logic? And how is this new media logic exported beyond the boundaries of (social or mass) media proper? The underlying principles, tactics, and strategies may be relat­ively simple to identify, but it is much harder to map the complex connections between plat­forms that distribute this logic: users that employ them, technologies that drive them, economic structures that scaffold them, and institutional bodies that incorporate them.

ABSTRACT Manuscript Type: Empirical Research Question/Issue: This paper investigates the connection between earnings management and corporate social responsibility (CSR). We argue that earnings management practices damage the collective interests of stakeholders; hence, managers who manipulate earnings can deal with stakeholder activism and vigilance by resorting to CSR practices. Research Findings/Insights: Using archival data from a multi‐national panel sample of 593 firms from 26 countries between 2002 and 2004, we find a positive impact of earnings management practices on CSR; this relationship holds for different robustness checks. Also, we demonstrate that the combination of earnings management and CSR has a negative impact on financial performance. Theoretical/Academic Implications: This study draws on a generalized agency theory where managers are seen as the agents of all stakeholders and the earnings management literature to highlight that CSR can be used to garner support from stakeholders and, therefore, provides an opportunity for entrenchment to those managers that manipulate earnings. As such, it suggests new avenues of research for both the corporate governance literature, as well as for the stakeholder perspective. Practitioner/Policy Implications: This study offers insights for policy makers and managers interested in enhancing CSR. For managers, our findings suggest that projecting a socially‐friendly image in order to disguise earnings management cannot be sustained over time due to the detrimental effect on financial performance. In addition, this study provides a warning signal to policy makers that certain practices geared toward raising a firm's CSR may simply be a mechanism for hindering other devious practices.

Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.

Indigenous and local knowledge systems as well as practitioners' knowledge can provide valid and useful knowledge to enhance our understanding of governance of biodiversity and ecosystems for human well-being. There is, therefore, a great need within emerging global assessment programs, such as the IPBES and other international efforts, to develop functioning mechanisms for legitimate, transparent, and constructive ways of creating synergies across knowledge systems. We present the multiple evidence base (MEB) as an approach that proposes parallels whereby indigenous, local and scientific knowledge systems are viewed to generate different manifestations of knowledge, which can generate new insights and innovations through complementarities. MEB emphasizes that evaluation of knowledge occurs primarily within rather than across knowledge systems. MEB on a particular issue creates an enriched picture of understanding, for triangulation and joint assessment of knowledge, and a starting point for further knowledge generation.

The capital asset pricing model (CAPM) of William Sharpe (1964) and John Lintner (1965) marks the birth of asset pricing theory (resulting in a Nobel Prize for Sharpe in 1990). Before their breakthrough, there were no asset pricing models built from first principles about the nature of tastes and investment opportunities and with clear testable predictions about risk and return. Four decades later, the CAPM is still widely used in applications, such as estimating the cost of equity capital for firms and evaluating the performance of managed portfolios. And it is the centerpiece, indeed often the only asset pricing model taught in MBA level investment courses. The attraction of the CAPM is its powerfully simple logic and intuitively pleasing predictions about how to measure risk and about the relation between expected return and risk. Unfortunately, perhaps because of its simplicity, the empirical record of the model is poor - poor enough to invalidate the way it is used in applications. The model's empirical problems may reflect true failings. (It is, after all, just a model.) But they may also be due to shortcomings of the empirical tests, most notably, poor proxies for the market portfolio of invested wealth, which plays a central role in the model's predictions. We argue, however, that if the market proxy problem invalidates tests of the model, it also invalidates most applications, which typically borrow the market proxies used in empirical tests. For perspective on the CAPM's predictions about risk and expected return, we begin with a brief summary of its logic. We then review the history of empirical work on the model and what it says about shortcomings of the CAPM that pose challenges to be explained by more complicated models.