A heuristic model for health-related autonomy based on health promotion ideas: the 'Health-Autonomy Interaction Model'.

Autonomy and health are central concepts in health promotion literature. Operational frameworks explaining their relationship are important to support health-related autonomy actions. This study presents a heuristic model describing the 'health-autonomy relationship' based on health promotion ideas, particularly Antonovsky's and Nordenfeldt's works. Central to the 'Health-Autonomy Interaction Model' is the existence of inter-related dimensions of autonomy: thought, intention and action, all influenced by negative and positive factors of physical and social environments (extrinsic factors) and personal resources to cope with life needs (intrinsic factors). Intrinsic factors comprise elements often included in definitions of negative (ill-health) and positive health (well-being and fitness), therefore establishing a bridge between conceptions of health and autonomy. Unique to this biopsychosocial framework is the differentiation between the potential for being autonomous and the degree of autonomy effectively achieved-the potential autonomy reflects the individual set of abilities to accomplish any task, while the expressed autonomy results from the extent to which this satisfies the demands of the environment and vital goals. Conceptualizing autonomy in this manner highlights that it is a multi-factorial capability referred to external conditions, reflecting a cluster of abilities to be and do things indicative of a self-empowered life. Concluding, our model moves the concept of health-related autonomy from a focus on 'disease' and 'incapacity' to a broader view in which the meaning of 'capacity' depends on the achievement of 'self-fulfillment' in given circumstances. The notion of autonomy is regarded as a precondition for health and an essential priority in health promotion.

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems.

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

An Analysis of Super-Net Heuristics in Weight-Sharing NAS.

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design and train the shared-weight backbone network, a.k.a. the super-net. Since heuristics substantially vary across different methods and have not been carefully studied, it is unclear to which extent they impact super-net training and hence the weight-sharing NAS algorithms. In this paper, we disentangle super-net training from the search algorithm, isolate 14 frequently-used training heuristics, and evaluate them over three benchmark search spaces. Our analysis uncovers that several commonly-used heuristics negatively impact the correlation between super-net and stand-alone performance, whereas simple, but often overlooked factors, such as proper hyper-parameter settings, are key to achieve strong performance. Equipped with this knowledge, we show that simple random search achieves competitive performance to complex state-of-the-art NAS algorithms when the super-net is properly trained.

New Heuristics to Stratify Applicants: Predictors of General Surgery Residency Applicant Step 1 Scores.

OBJECTIVE: In 2022, United States Medical Licensing Examination (USMLE) Step 1 scores will become pass/fail. This may be problematic, as residency programs heavily rely on USMLE Step 1 scores as a metric when determining interview invitations. This study aimed to assess candidate application metrics associated with USMLE Step 1 scores to offer programs new cues for stratifying applicants. DESIGN: Retrospective cohort study analyzing interviewed applicants to one general surgery residency program in 2019 and 2020. Applicant data analyzed included USMLE Step 1 scores, number of publications, clerkship scores, letter of recommendation scores (out of 2, scored by 0.25 interval), interview overall score (out of 5, scored by integer level), and standardized question score (out of 10). Each year, applicant's answers to one standardized behavioral question during their interview were scored by interviewers. SETTING: Tertiary medical center, academic general surgery residency program. PARTICIPANTS: Interviewed applicants at one general surgery residency program whose applications contained complete demographic information (203 out of 247). RESULTS: Multiple Linear Regression revealed that higher surgical clerkship (ß = 0.19, p = 0.006) and higher standardized interview question (ß = 0.32, p < 0.001) scores were positively associated with applicant USMLE Step 1 score (F[7, 195] = 6.61, p < 0.001, R2 = 0.19). Letter of recommendation score, number of peer reviewed publications, gender, race, and applicant type (preliminary/categorical) were not associated with USMLE Step 1 scores. CONCLUSIONS: With USMLE Step 1 scores transitioning to pass/fail, surgical residency programs need new selection heuristics. Surgery clerkship scores and standardized behavioral questions answered by applicants prior to the interview could provide a holistic view of applicants and help programs better stratify candidates without USMLE Step 1 scores.

Heuristics for the sustainable harvest of wildlife in stochastic social-ecological systems.

Sustainable wildlife harvest is challenging due to the complexity of uncertain social-ecological systems, and diverse stakeholder perspectives of sustainability. In these systems, semi-complex stochastic simulation models can provide heuristics that bridge the gap between highly simplified theoretical models and highly context-specific case-studies. Such heuristics allow for more nuanced recommendations in low-knowledge contexts, and an improved understanding of model sensitivity and transferability to novel contexts. We develop semi-complex Management Strategy Evaluation (MSE) models capturing dynamics and variability in ecological processes, monitoring, decision-making, and harvest implementation, under a diverse range of contexts. Results reveal the fundamental challenges of achieving sustainability in wildlife harvest. Environmental contexts were important in determining optimal harvest parameters, but overall, evaluation contexts more strongly influenced perceived outcomes, optimal harvest parameters and optimal harvest strategies. Importantly, simple composite metrics popular in the theoretical literature (e.g. focusing on maximizing yield and population persistence only) often diverged from more holistic composite metrics that include a wider range of population and harvest objectives, and better reflect the trade-offs in real world applied contexts. While adaptive harvest strategies were most frequently preferred, particularly for more complex environmental contexts (e.g. high uncertainty or variability), our simulations map out cases where these heuristics may not hold. Despite not always being the optimal solution, overall adaptive harvest strategies resulted in the least value forgone, and are likely to give the best outcomes under future climatic variability and uncertainty. This demonstrates the potential value of heuristics for guiding applied management.

A heuristic perspective on non-variational free energy modulation at the sleep-like edge.

BACKGROUND: The variational Free Energy Principle (FEP) establishes that a neural system minimizes a free energy function of their internal state through environmental sensing entailing beliefs about hidden states in their environment. PROBLEM: Because sensations are drastically reduced during sleep, it is still unclear how a self-organizing neural network can modulate free energy during sleep transitions. GOAL: To address this issue, we study how network's state-dependent changes in energy, entropy and free energy connect with changes at the synaptic level in the absence of sensing during a sleep-like transition. APPROACH: We use simulations of a physically plausible, environmentally isolated neuronal network that self-organize after inducing a thalamic input to show that the reduction of non-variational free energy depends sensitively upon thalamic input at a slow, rhythmic Poisson (delta) frequency due to spike timing dependent plasticity. METHODS: We define a non-variational free energy in terms of the relative difference between the energy and entropy of the network from the initial distribution (prior to activity dependent plasticity) to the nonequilibrium steady-state distribution (after plasticity). We repeated the analysis under different levels of thalamic drive - as defined by the number of cortical neurons in receipt of thalamic input. RESULTS: Entraining slow activity with thalamic input induces a transition from a gamma (awake-like state) to a delta (sleep-like state) mode of activity, which can be characterized through a modulation of network's energy and entropy (non-variational free energy) of the ensuing dynamics. The self-organizing response to low and high thalamic drive also showed characteristic differences in the spectrum of frequency content due to spike timing dependent plasticity. CONCLUSIONS: The modulation of this non-variational free energy in a network that self-organizes, seems to be an organizational network principle. This could open a window to new empirically testable hypotheses about state changes in a neural network.

Concepts of God and Germs: Social Mechanisms and Cognitive Heuristics.

Previous research has shown that the more individuals view observable entities as animate, the more those entities are associated with having psychological and physiological experiences. This study examined the relationship between children's animistic and anthropomorphic reasoning for concepts of unobservable scientific (i.e., germ) and religious (i.e., God) entities. This study further explored how children's conceptions vary according to the social learning opportunities (i.e., discourse, rituals) parents reportedly create. Parent-child dyads with young children from diverse ethnic and religious backgrounds participated. Three central findings emerged. First, children readily associated God with psychobiological characteristics but did not do so to the same extent for germs. Second, children applied more psychobiological properties to both entity types when they believed that the entity was animate. Third, engaging in rituals and discourse with parents was indirectly related to children's concepts of God but not related to their concepts of germs. Overall, this study presented support for a connection between children's animistic and anthropomorphic reasoning for unobservable entities, and an indirect effect of cultural input on this reasoning. The implications of these findings will be discussed.

Policy responses to COVID-19 present a window of opportunity for a paradigm shift in global health policy: An application of the Multiple Streams Framework as a heuristic.

Drawing on Kingdon's Multiple Streams Framework as a heuristic, this article reviews the three streams - problems, policies, and politics - as applied to the adoption of economic policies in response to the socioeconomic impacts of COVID-19. In doing so, we argue that we are currently presented with a window of opportunity to better address the social determinants of health. First, through assessing the problem stream, an understanding of inequity as a problem gained wider recognition through the disproportionate impacts of COVID-19. Second, in the policy stream, we demonstrate that appropriate and unprecedented policies can be enacted even in the face of changing evidence or evidentiary uncertainty, which are needed to address upstream factors that influence health. Lastly, in the politics stream, we demonstrate that addressing a public health 'problem' can be well-received by the public, making it politically viable. However, it is important to ensure the 'problem' is clearly relayed to the public and that this information is not perceived to change, as this can undermine trust. The social, political, and behavioural lessons presented by the COVID-19 pandemic should be drawn on in this pivotal moment for global public health.

Working (it) Out: An Heuristic Enquiry Into Psychotherapy and Physical Exercise.

Psychotherapy is predominantly a sedentary practice that, with rare exceptions, does not involve much physical activity on the part of either the client or the therapist. In response to this situation, the article examines three concerns: the impact of sedentarism on psychotherapists; the disconnection between the evidence of the benefit of physical exercise on psychological wellbeing and the predominant focus in psychotherapy on the sedentary mind; and the implications of the disconnection between psychotherapists' own minds and bodies. of the method employed was an heuristic enquiry conducted by the first author (reflected in the "I" and the "my" voice in the article), under the supervision of the second author. Drawing on and interweaving relevant literature throughout, the enquiry explores the first author's own relationship with exercise and, specifically, boxing, as both a practice (i.e., physical exercise) and as a metaphor for the heuristic research process (a psychological and intellectual exercise).

Hypothalamic Heuristics for Survival.

Hypothalamic neurons implicated in energy homeostasis (Agrp, POMC, orexin, MCH) display fast, nutrient-independent dynamics. They do not simply mirror the slowly changing internal nutrient levels, but adapt rapidly to diverse external cues. Moreover, instead of eating, neonatal Agrp cells stimulate mother-attracting vocalisations, illustrating heuristic energy control beyond nutrient sensing or dietary self-control.

ALOHA: developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design.

BACKGROUND: Dietary supplements (DSs) are widely used. However, consumers know little about the safety and efficacy of DSs. There is a growing interest in accessing health information online; however, health information, especially online information on DSs, is scattered with varying levels of quality. In our previous work, we prototyped a web application, ALOHA, with interactive graph-based visualization to facilitate consumers' browsing of the integrated DIetary Supplement Knowledge base (iDISK) curated from scientific resources, following an iterative user-centered design (UCD) process. METHODS: Following UCD principles, we carried out two design iterations to enrich the functionalities of ALOHA and enhance its usability. For each iteration, we conducted a usability assessment and design session with a focus group of 8-10 participants and evaluated the usability with a modified System Usability Scale (SUS). Through thematic analysis, we summarized the identified usability issues and conducted a heuristic evaluation to map them to the Gerhardt-Powals' cognitive engineering principles. We derived suggested improvements from each of the usability assessment session and enhanced ALOHA accordingly in the next design iteration. RESULTS: The SUS score in the second design iteration decreased to 52.2 ± 11.0 from 63.75 ± 7.2 in our original work, possibly due to the high number of new functionalities we introduced. By refining existing functionalities to make the user interface simpler, the SUS score increased to 64.4 ± 7.2 in the third design iteration. All participants agreed that such an application is urgently needed to address the gaps in how DS information is currently organized and consumed online. Moreover, most participants thought that the graph-based visualization in ALOHA is a creative and visually appealing format to obtain health information. CONCLUSIONS: In this study, we improved a novel interactive visualization platform, ALOHA, for the general public to obtain DS-related information through two UCD design iterations. The lessons learned from the two design iterations could serve as a guide to further enhance ALOHA and the development of other knowledge graph-based applications. Our study also showed that graph-based interactive visualization is a novel and acceptable approach to end-users who are interested in seeking online health information of various domains.

Empirical and Heuristic Phenomenological Case Study of the HeartMath Global Coherence Initiative.

Along with the creativity of vast technological advances, humanity's endemic destructiveness continues. Planetary healing needs motivated this research. The aim was an empirical and heuristic phenomenological investigation into and an evaluation of the theoretical and technological implications of the HeartMath Global Coherence Initiative. The single case study, and limited amount of data, indicated the null hypothesis. Methodology included HeartMath Inner Balance tool and newly developed Global Coherence application (app). Data collection involved linked empirical measures and experiential journaling. Quantitative data analysis, which consisted of statistical analysis of correlations between six existing Global Coherence magnetometers and empirical measures of meditation records, from Inner Balance and Global coherence apps, respectively, yielded unexpected findings, both significant and insignificant, in the form of trends towards global and local group coherence, respectively. Qualitative findings essentially revealed variations on the, interrelated, consciousness themes of wholeness, holistic healing, energy healing and meditation. In addition to various limitations and implications, interpretation of integrative findings indicated theoretical and practical support for the HeartMath mission and vision of developing and promoting personal, social and global coherence.

Artificial intelligence-enabled healthcare delivery.

In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.

Social media in nursing and midwifery education: A mixed study systematic review.

AIM: To synthesize evidence on the effectiveness of social media in nursing and midwifery education. BACKGROUND: Social media are being explored to see if these online tools can support teaching, learning, and assessment. DESIGN: A mixed study systematic review. DATA SOURCES: A systematic search of PubMed, MEDLINE, CINAHL, Scopus, and ERIC was run in January 2016. An updated search was run in June 2017. No date limits were applied. METHODS: Titles, abstracts, and full papers were screened against inclusion criteria by two independent reviewers, who extracted and quality assessed data. Synthesis followed a sequential explanatory approach. RESULTS: Twelve studies were included. Social media seemed to support students to acquire new knowledge and skills. The learning process centred on the interactive nature of the platforms which allow information to be dynamically shared and discussed in near real time. The characteristics of social media enabled social support and a more student-centred setting, which appeared to enhance collaborative learning, although information quality was sometimes problematic. Learning via social media was underpinned by how well the educational interventions were organized, digital literacy and e-Professionalism of students and faculty, the accessibility of the online applications, and personal motivation. CONCLUSION: This review provides the first rigorous synthesis of social media in nursing and midwifery education. A new Social Media Learning Model was conceptualized to aid our understanding of learning via this technology. Knowledge gaps are identified and recommendations on how to capitalize on social media to improve learning in higher and continuing education provided.

A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery.

BACKGROUND: Breast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objective is to propose a systematic framework to identify the clinical and non-clinical variables that influence the receipt of unexpected radiotherapy treatment by means of Bayesian networks and a proposed heuristic approach. METHODS: We used cancer registry data of Detroit, San Francisco-Oakland, and Atlanta from years 2007-2012 downloaded from the Surveillance, Epidemiology, and End Results Program. The samples had patients diagnosed with in situ and early invasive cancer with 14 clinical and non-clinical variables. Bayesian networks were fitted to the data of each region and systematically analyzed through the proposed Zoom-in heuristic. A comparative analysis with logistic regressions is also presented. RESULTS: For Detroit, patients under stage 0, grade undetermined, histology lobular carcinoma in situ, and age between 26-50 were found more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients with age between 51-75, and grade II were found to be more likely to receive breast-conservation surgery with radiotherapy. For San Francisco-Oakland, patients under stage 0, grade undetermined, and age >75 are more likely to receive BCS. For stages I, IIA, and IIB patients with age >75 are more likely to receive breast-conservation surgery without radiotherapy. For Atlanta, patients under stage 0, grade undetermined, year 2011, and primary site C509 are more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients in year 2011, and grade III are more likely to receive breast-conservation surgery without radiotherapy. CONCLUSION: For in situ breast cancer and early invasive breast cancer, the results are in accordance with the guidelines and very well demonstrates the usefulness of the Zoom-in heuristic in systematically characterizing a group receiving a treatment. We found a subset of the population from Detroit with ductal carcinoma in situ for which breast-conservation surgery without radiotherapy was received, but potential reasons for this treatment are still unknown.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Participatory Heuristic Evaluation of the Second Iteration of the eWALL Interface Application.

The number of people having a chronic disease is increasing. Telehealth may provide an alternative to traditional medicine as telehealth solutions have shown to have a positive influence on quality of live and to decrease the number of hospital visits. A new telehealth solution is the eWALL system. Previously, the eWALL interface application has been evaluated using participatory heuristic evaluation (PHE). The previous round of PHE lead to drastic changes of the eWALL interface application. Consequently, a second round of PHE was performed. Five usability experts and two work-domain professionals inspected the eWALL interface application and identified usability problems (n = 384). The work domain professionals had a tendency to use other heuristics than the usability experts highlighting the relevance of using PHE in an interface development process.

A co-design process developing heuristics for practitioners providing end of life care for people with dementia.

BACKGROUND: The end of life for someone with dementia can present many challenges for practitioners; such as, providing care if there are swallowing difficulties. This study aimed to develop a toolkit of heuristics (rules-of-thumb) to aid practitioners making end-of-life care decisions for people with dementia. METHODS: An iterative co-design approach was adopted using a literature review and qualitative methods, including; 1) qualitative interviews and focus groups with family carers and 2) focus groups with health and care professionals. Family carers were recruited from a national charity, purposively sampling those with experience of end-of-life care for a person with dementia. Health and care professionals were purposively sampled to include a broad range of expertise including; general practitioners, palliative care specialists, and geriatricians. A co-design group was established consisting of health and social care experts and family carers, to synthesise the findings from the qualitative work and produce a toolkit of heuristics to be tested in practice. RESULTS: Four broad areas were identified as requiring complex decisions at the end of life; 1) eating/swallowing difficulties, 2) agitation/restlessness, 3) ending life-sustaining treatment, and 4) providing "routine care" at the end of life. Each topic became a heuristic consisting of rules arranged into flowcharts. Eating/swallowing difficulties have three rules; ensuring eating/swallowing difficulties do not come as a surprise, considering if the situation is an emergency, and considering 'comfort feeding' only versus time-trialled artificial feeding. Agitation/restlessness encourages a holistic approach, considering the environment, physical causes, and the carer's wellbeing. Ending life-sustaining treatment supports practitioners through a process of considering the benefits of treatment versus quality-of-life and comfort. Finally, a heuristic on providing routine care such as bathing, prompts practitioners to consider adapting the delivery of care, in order to promote comfort and dignity at the end of life. CONCLUSIONS: The heuristics are easy to use and remember, offering a novel approach to decision making for dementia end-of-life care. They have the potential to be used alongside existing end-of-life care recommendations, adding more readily available practical assistance. This is the first study to synthesise experience and existing evidence into easy-to-use heuristics for dementia end-of-life care.

An algorithm for automated layout of process description maps drawn in SBGN.

MOTIVATION: Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. RESULTS: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. AVAILABILITY AND IMPLEMENTATION: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). CONTACT: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.