Behavioral Science Expertise Research Articles

Evaluation of Code Generation for Simulating Participant Behavior in Experience Sampling Method by Iterative In-Context Learning of a Large Language Model

The Experience Sampling Method (ESM) is commonly used to understand behaviors, thoughts, and feelings in the wild by collecting self-reports. Sustaining sufficient response rates, especially in long-running studies remains challenging. To avoid low response rates and dropouts, experimenters rely on their experience, proposed methodologies from earlier studies, trial and error, or the scarcely available participant behavior data from previous ESM protocols. This approach often fails in finding the acceptable study parameters, resulting in redesigning the protocol and repeating the experiment. Research has shown the potential of machine learning to personalize ESM protocols such that ESM prompts are delivered at opportune moments, leading to higher response rates. The corresponding training process is hindered due to the scarcity of open data in the ESM domain, causing a cold start, which could be mitigated by simulating participant behavior. Such simulations provide training data and insights for the experimenters to update their study design choices. Creating this simulation requires behavioral science, psychology, and programming expertise. Large language models (LLMs) have emerged as facilitators for information inquiry and programming, albeit random and occasionally unreliable. We aspire to assess the readiness of LLMs in an ESM use case. We conducted research using GPT-3.5 turbo-16k to tackle an ESM simulation problem. We explored several prompt design alternatives to generate ESM simulation programs, evaluated the output code in terms of semantics and syntax, and interviewed ESM practitioners. We found that engineering LLM-enabled ESM simulations have the potential to facilitate data generation, but they perpetuate trust and reliability challenges.

An approach for embedding behavioural science in antimicrobial resistance One Health research

Antimicrobial Resistance (AMR) is a global health emergency that threatens modern medicine and incurs great cost to human health. The World Health Organization as part of a quadripartite joint initiative with the Food and Agriculture Organization of the United Nations, World Organisation for Animal Health, and United Nations Environment Programme, has recently published a One Health Priority Research Agenda for AMR. In this article we present a multidisciplinary approach, proposed by behavioural science experts, One Health experts and AMR experts to support the implementation of the Priority Research Agenda. We review, using specific examples of complex interventions designed to tackle AMR in which behavioural science has been embedded, five main steps: Define – what behaviours are a priority in each context; Diagnose - What are the barriers and enablers to the behaviours prioritised? Design - what interventions exist and what new or enhanced interventions could work to tackle the barriers identified? and, Implement and Evaluate the intervention(s). The approach presented will be useful for funders and researchers who wish to incorporate methods, frameworks and insights from the behavioural sciences into research plans, proposals and protocols in relation to a multisectoral One Health agenda and produce findings that are more relevant to policymakers.

Specifying how intervention content is communicated: Development of a Style of Delivery Ontology.

Background: Investigating and enhancing the effectiveness of behaviour change interventions requires detailed and consistent specification of all aspects of interventions. We need to understand not only their content, that is the specific techniques, but also the source, mode, schedule, and style in which this content is delivered. Delivery style refers to the manner by which content is communicated to intervention participants. This paper reports the development of an ontology for specifying the style of delivery of interventions that depend on communication. This forms part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change intervention scenarios. Methods: The Style of Delivery Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project, with seven key steps: 1) defining the scope of the ontology, 2) identifying key entities and developing their preliminary definitions by reviewing 100 behaviour change intervention evaluation reports and existing classification systems, 3) refining the ontology by piloting the ontology through annotations of 100 reports, 4) stakeholder review by eight behavioural science and public health experts, 5) inter-rater reliability testing through annotating 100 reports using the ontology, 6) specifying ontological relationships between entities, and 7) disseminating and maintaining the ontology. Results: The resulting ontology is a five-level hierarchical structure comprising 145 unique entities relevant to style of delivery. Key areas include communication processes, communication styles, and attributes of objects used in communication processes. Inter-rater reliability for annotating intervention evaluation reports was α=0.77 (good) for those familiar with the ontology and α=0.62 (acceptable) for those unfamiliar with it. Conclusions: The Style of Delivery Ontology can be used for both annotating and describing behaviour change interventions in a consistent and coherent manner, thereby improving evidence comparison, synthesis, replication, and implementation of effective interventions.

Investigating the relationship between organizational culture and human resource productivity (case study of government department employees in Ilam province)

Background and purpose: Organizational culture and human resource productivity are among the categories that have attracted the attention of many behavioral science and management experts in recent years. The current research aimed to investigate the relationship between organizational culture and human resource productivity among government department employees in Ilam province. Methodology: The research is correlational and descriptive, and its population consists of all the education workers of Ilam province; 164 employees were selected as a sample through Cochran's sampling formula. To collect data, a standard questionnaire including general questions about the status of the respondents and specialized questions about organizational culture and human resource productivity in education and training was used in the form of questions with a Likert scale, and Cronbach's alpha method was used to check its reliability. In data analysis, descriptive statistics and inferential statistics methods such as t-test, analysis of variance tests with a significance level of 05%, and Pearson's correlation coefficient were used to correlate variables using SPSS software. Results: The findings of the research that took place in education in Ilam province indicate a significant and direct relationship between organizational culture and human resource productivity, and culture directly impacts the organization's performance and effectiveness. Conclusion: The results showed a direct and meaningful relationship between the existence of close and intimate relationships between human resources and the organization's productivity.

The Vaccination Concerns in COVID-19 Scale (VaCCS): Development and validation.

Vaccines are highly effective in minimizing serious cases of COVID-19 and pivotal to managing the COVID-19 pandemic. Despite widespread availability, vaccination rates fall short of levels required to bring about widespread immunity, with low rates attributed to vaccine hesitancy. It is therefore important to identify the beliefs and concerns associated with vaccine intentions and uptake. The present study aimed to develop and validate, using the AMEE Guide, the Vaccination Concerns in COVID-19 Scale (VaCCS), a comprehensive measure of beliefs and concerns with respect to COVID-19 vaccines. In the scale development phase, samples of Australian (N = 53) and USA (N = 48) residents completed an initial open-response survey to elicit beliefs and concerns about COVID-19 vaccines. A concurrent rapid literature review was conducted to identify content from existing scales on vaccination beliefs. An initial pool of items was developed informed by the survey responses and rapid review. The readability and face validity of the item pool was assessed by behavioral science experts (N = 5) and non-experts (N = 10). In the scale validation phase, samples of Australian (N = 522) and USA (N = 499) residents completed scaled versions of the final item pool and measures of socio-political, health beliefs and outcomes, and trait measures. Exploratory factor analysis yielded a scale comprising 35 items with 8 subscales, and subsequent confirmatory factor analyses indicated acceptable fit of the scale structure with the data in each sample and factorial invariance across samples. Concurrent and predictive validity tests indicated a theoretically and conceptually predictable pattern of relations between the VaCCS subscales with the socio-political, health beliefs and outcomes, and trait measures, and key subscales predicted intentions to receive the COVID-19 vaccine. The VaCCS provides a novel measure to assess beliefs and concerns toward COVID-19 vaccination that researchers and practitioners can use in its entirety or select specific sub-scales to use according to their needs.

Expert predictions of societal change: Insights from the world after COVID project.

How do experts in human behavior think the world might change after the coronavirus (COVID-19) pandemic? What advice do they have for the postpandemic world? Is there a consensus on the most significant psychological and societal changes ahead? To answer these questions, we analyzed interviews from the World After COVID Project-reflections of more than 50 of the world's top behavioral and social science experts, including fellows of National Academies and presidents of major scientific societies. These experts independently shared their thoughts on possible psychological changes in society in the aftermath of the COVID-19 pandemic and provided recommendations how to respond to the new challenges and opportunities these shifts may bring. We distilled these predictions and suggestions via human-coded analyses and natural language processing techniques. In general, experts showed little overlap in their predictions, except for convergence on a set of social/societal themes (e.g., greater appreciation for social connection, increasing political conflict). Half of the experts approached their post-COVID predictions dialectically, highlighting both positive and negative features of the same domain of change, and many expressed uncertainty in their predictions. The project offers a time capsule of experts' predictions for the effects of the pandemic on a wide range of outcomes. We discuss the implications of heterogeneity in these predictions, the value of uncertainty and dialecticism in forecasting, and the value of balancing explanation with predictions in expert psychological judgment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A collaborative clinical case conference model for teaching social and behavioral science in medicine: an action research study

BackgroundEffective social and behavioral sciences teaching in medical education requires integration with clinical experience, as well as collaboration between social and behavioral sciences experts and clinical faculty. However, teaching models for achieving this integration have not been adequately established, nor has the collaboration process been described. This study aims to propose a collaborative clinical case conference model to integrate social and behavioral sciences and clinical experience. Additionally, we describe how social and behavioral science experts and clinical faculty collaborate during the development of the teaching method.MethodsA team of medical teachers and medical anthropologists planned for the development of a case conference based on action research methodology. The initial model was planned for a 3-h session, similar to a Clinicopathological Conference (CPC) structure. We evaluated each session based on field notes taken by medical anthropologists and post-session questionnaires that surveyed participants’ reactions and points of improvement. Based on the evaluation, a reflective meeting was held to discuss revisions for the next trial. We incorporated the development process into undergraduate medical curricula in clinical years and in a postgraduate and continuous professional development session for residents and certified family physicians in Japan. We repeated the plan-act-observe-reflection process more than 15 times between 2015 and 2018.ResultsThe development of the collaborative clinical case conference model is summarized in three phases: Quasi-CPC, Interactive, and Co-constructive with unique structures and underlying paradigms. The model successfully contributed to promoting the participants’ recognition of the clinical significance of social and behavioral sciences. The case preparation entailed unique and significant learning of how social and behavioral sciences inform clinical practice. The model development process promoted the mutual understanding between clinical faculty and anthropologists, which might function as faculty development for teachers involved in social and behavioral sciences teaching in medical education.ConclusionsThe application of appropriate conference models and awareness of their underlying paradigms according to educational situations promotes the integration of social and behavioral sciences with clinical medicine education. Faculty development regarding social and behavioral sciences in medical education should focus on collaboration with scholars with different paradigmatic orientations.

Burnout Among Healthcare Workers in the COVID 19 Era: A Review of the Existing Literature.

In the current period of global public health crisis due to the COVID-19, healthcare workers are more exposed to physical and mental exhaustion – burnout – for the torment of difficult decisions, the pain of losing patients and colleagues, and the risk of infection, for themselves and their families. The very high number of cases and deaths, and the probable future “waves” raise awareness of these challenging working conditions and the need to address burnout by identifying possible solutions. Measures have been suggested to prevent or reduce burnout at individual level (physical activity, balanced diet, good sleep hygiene, family support, meaningful relationships, reflective practices and small group discussions), organizational level (blame-free environments for sharing experiences and advices, broad involvement in management decisions, multi-disciplinary psychosocial support teams, safe areas to withdraw quickly from stressful situations, adequate time planning, social support), and cultural level (involvement of healthcare workers in the development, implementation, testing, and evaluation of measures against burnout). Although some progress has been made in removing the barrier to psychological support to cope with work-related stress, a cultural change is still needed for the stigma associated with mental illness. The key recommendation is to address the challenges that the emergency poses and to aggregate health, well-being and behavioral science expertise through long term researches with rigorous planning and reporting to drive the necessary cultural change and the improvement of public health systems.

Specifying who delivers behaviour change interventions: development of an Intervention Source Ontology.

Background: Identifying how behaviour change interventions are delivered, including by whom, is key to understanding intervention effectiveness. However, information about who delivers interventions is reported inconsistently in intervention evaluations, limiting communication and knowledge accumulation. This paper reports a method for consistent reporting: The Intervention Source Ontology. This forms one part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change interventions . Methods: The Intervention Source Ontology was developed following methods for ontology development and maintenance used in the Human Behaviour-Change Project, with seven key steps: 1) define the scope of the ontology, 2) identify key entities and develop their preliminary definitions by reviewing existing classification systems (top-down) and reviewing 100 behaviour change intervention reports (bottom-up), 3) refine the ontology by piloting the preliminary ontology on 100 reports, 4) stakeholder review by 34 behavioural science and public health experts, 5) inter-rater reliability testing of annotating intervention reports using the ontology, 6) specify ontological relationships between entities and 7) disseminate and maintain the Intervention Source Ontology. Results: The Intervention Source Ontology consists of 140 entities. Key areas of the ontology include Occupational Role of Source, Relatedness between Person Source and the Target Population, Sociodemographic attributes and Expertise. Inter-rater reliability was found to be 0.60 for those familiar with the ontology and 0.59 for those unfamiliar with it, levels of agreement considered 'acceptable'. Conclusions: Information about who delivers behaviour change interventions can be reliably specified using the Intervention Source Ontology. For human-delivered interventions, the ontology can be used to classify source characteristics in existing behaviour change reports and enable clearer specification of intervention sources in reporting.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology.

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application.

BackgroundArtificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices.PurposesBy using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP).MethodsThe project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists.ResultsIntervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data.ConclusionsAI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

Retrospective coding of health care professional cancer screening behaviours and of real‐world interventions designed to support them: Identifying recommendations to optimize intervention design

Screening can detect cancer earlier. Uptake of breast, cervical, and bowel cancer screening in England is below 75%. This study identifies the barriers and facilitators underpinning HCP screening behaviours which can support screening uptake, and reviews the design of real-world interventions targeting these, assessing for congruence between the two. The aim was to provide recommendations to improve the design of interventions. Barriers/facilitators were identified by a literature review and qualitatively coded using the theoretical domains framework (TDF). Interventions were identified by stakeholders and coded using the behaviour change wheel and the taxonomy of behaviour change techniques. Congruence was assessed through comparing the intervention designs with behavioural science experts' recommendations which link the TDF domains to intervention design. Recommendations targeted missed opportunities. Barriers/facilitators were extracted from 60 papers and most frequently coded to the TDF domains: environmental context and resources, knowledge and beliefs about consequences. Thirty-one interventions were identified, most frequently education, training or enablement functions, delivered via communication/marketing or service provision, and using BCTs designed to shape knowledge or highlight the consequences of or antecedents to screening. Intervention design was largely congruent with recommendations. However, there was less use of persuasion and modelling intervention functions and a reliance on BCTs such as providing instruction when other BCTs could be considered. Recommendations include to consider a broader range of intervention functions and BCTs, particularly for training interventions which should make use of recommended BCTs such as 'graded tasks'.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Preventing COVID-19 and Its Sequela: “There Is No Magic Bullet... It's Just Behaviors”

Preventing COVID-19 and Its Sequela: “There Is No Magic Bullet... It's Just Behaviors”

Reducing our waste size

Reducing our waste size

Prioritizing barriers to adopt circular economy in construction and demolition waste management

Construction and demolition (C&D) waste dreadful impacts increasingly cause public concerns. Aiming to boost resource exploitation efficiency, circular economy is contemplated a pertinent C&D waste management practice. However, transitioning to circular economy in C&D waste management is hindered by barriers which are to be found and overcome to adeptly handle this kind of waste. In this paper 22 potential crisp barriers are identified through reviewing the seminal literature which could be categorized under three dimensions of behavioral, technical, and legal. Six experts in behavioral, technical, and legal sciences working in sustainable C&D projects were asked to rate the dimensions and the barriers through a questionnaire survey on the results of which fuzzy TOPSIS method is applied to prioritize the barriers followed by advancing a framework to facilitate pushing C&D waste management toward circular economy. The study concluded that from behavioral, technical, and legal perspectives, using finitely recyclable construction materials; ineffective C&D wastes dismantling, sorting, transporting, and recovering processes; and using finitely recyclable construction materials are ranked first. Also from an aggregate perspective, agency and ownership issues in C&D waste management, lack of integration of sustainable C&D waste management, and uncertain aftermaths of moving toward circular economy in C&D waste management are three high priority barriers which should be removed before transforming current linear economy to circular economy in C&D waste management.

Addressing Behaviors That Lead to Sharing Fake News

Some recent behavioral science research has shown why people lie, and how we can prevent them from lying. Given the alarming amount of fake news in the US public sphere, a group of behavioral science experts has sought to combine the available research in a proposed intervention, the Pro-Truth Pledge, to help address this problem. The pledge asks signees to commit to 12 behaviors that research in behavioral science shows correlate with an orientation toward truthfulness. Early results show both that private citizens and public figures are willing to take the pledge, and self-reported and external observations of present case study exhibit evidence of the effectiveness of the pledge.

BETTER ORAL HEALTH TO ALL

Despite vast improvement in global oral health, problems persist in many populations and communities particularly among the underprivileged in both and developed and developing countries. Dental caries and periodontal diseases have for years burdened the majority of populations with heavy treatment and financial needs. Dental caries is still a major oral health problem in most industrialized countries as it affects 60-90% of school-aged children and the vast majority of adults. According to the World Health Organization oral diseases are the fourth most expensive to treat. Their treatment costs exceed those of cancer, heart and coronary diseases, stroke and dementia in most industrialized countries. It has been estimated that the European Union countries used 79 billion euros for oral health care in 2012. Should the present trend continue the costs could exceed 93 billion euros in 2020. Oral and dental care, as known in the developed world, is not available to 90% of the global population. The millions living in poverty do not have access to emergency dental care or to preventive measures to help them in their own oral health. It has been estimated that 80% of dentists in the world are serving 20% of the global population. Given their prevalence worldwide oral diseases represent major public health problems causing pain, suffering and reduced quality of life. Generally, studies on dental caries and periodontal diseases have shown a decreasing trend of oral diseases in developed countries. On the other hand assessing the situation in developing countries is characterized by lack of continuous and reliable data despite the recommendations of WHO. To provide or even to plan dental services required to meet the needs, oral health need assessments and surveys are necessary both locally and nationally. The major oral diseases are today understood more clearly than ever as behavior-related diseases. They are preventable through individual’s own actions supported by the health professionals. Generally, health behaviors, either health-enhancing or health-detrimental, are mostly derived through norms, values and goals of the family. The family plays the primary role in the acquisition, modification and improvement of health behavior. However, maintaining let alone changing health related behavior is a vast challenge. Everyday life is full of health related risks known or unknown to the individuals. A health-related behavior is not a simple matter of freedom of choice rather than reflection of one’s lifestyle. Lifestyle with different risks being also determinants of different diseases should be understood as an expression of the cultural and social environment in which people live and work. This concept bears significant impact on oral health. The core group of modifiable risk factors for the chronic diseases often cluster in the same population groups and individuals. These factors include smoking, alcohol consumption, diet, hygiene and stress. Controlling one or a small number of risk factors have a major impact on a number of chronic diseases including oral diseases. The behavioral science experts are of opinion that only comprehensive and integrated common-risk-factor-based health promotion activities can enhance oral health and its equity as a part of general health. Are health professionals ready to assume their responsibility for promoting better oral health?

Behavior change interventions: the potential of ontologies for advancing science and practice.

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.