Perceived influences on the career choices of children and youth: an exploratory study

Children’s understanding of factors influencing their career choices was examined. Seventy-two children, in grades kindergarten, 4, and 8, responded to questions about their perceptions of career influences. Responses were coded to capture the nature of the influences identified, including the global versus specific and linear versus interacting nature of these influences. Further, influences were coded as existing proximal versus distal to the child. Results indicate that older children identified more career influences that were either specific or categorical and interacted in dynamic ways. No evidence was found for older children offering influences that existed at a systems level of organization.Accepted manuscrip

Exploratory analysis of resource demand and the environmental footprint of future aquaculture development using Life Cycle Assessment

Increases in fish demand in the coming decades are projected to be largely met by growth of aquaculture. However, increased aquaculture production is linked to higher demand for natural resources and energy as well as emissions to the environment. This paper explores the use of Life Cycle Assessment to improve knowledge of potential environmental impacts of future aquaculture growth. Different scenarios of future aquaculture development are taken into account in calculating the life cycle environmental impacts. The environmental impact assessments were built on Food and Agriculture Organization statistics in terms of production volume of different species, whereas the inputs and outputs associated with aquaculture production systems were sourced from the literature. The matrix of input-output databases was established through the Blue Frontiers study

Domainbuilder: The knowledge authoring system for slide tutor intelligent tutoring system[version 1; peer review: 1 approved, 1 approved with reservations]

One of the major challenges in the development of medical Intelligent Tutoring Systems (ITS) is the development of authored content, a time-consuming process that requires participation of discipline experts. In this publication, we describe the development of software systems called DomainBuilder and TutorBuilder, designed to streamline and simplify the authoring process for general medical ITSs. The aim of these systems is to allow physicians without programming or ITSs background to create a domain knowledge base and author tutor cases in a time efficient manner. DomainBuilder combined knowledge authoring, case authoring, and validation tasks into a single work environment, enabling multiple authoring strategies. Natural Language Processing (NLP) methods were integrated for parsing existing clinical reports to speed case authoring. Similarly, TutorBuilder was designed to allow users to customize all aspects of ITSs, including user interface, pedagogic module, feedback module, etc. Both systems underwent formal usability studies with physicians specializing in dermatology. Open-ended questions assessed usability of the system and satisfaction with its features. Incorporating feedback from usability studies, DomainBuilder and TutorBuilder systems were deployed and used across multiple universities to create customized medical tutoring curriculum. Overall, both systems were well received by medical professionals participating in usability studies with participants highlighting ease of utilization and clarity of presentation. Usability study participants were able to successfully use the system for the authoring tasks. DomainBuilder and TutorBuilder are novel tools that combine comprehensive aspects of content creation, including creation of domain ontologies, case authoring, and validation

An information model for computable cancer phenotypes

Background: Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical “deep phenotype” of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine. Methods: Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions. Results: The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient’s disease. Conclusions: We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis

Asset or liability? Aquaculture in a natural disaster prone area

Cyclone Sidr, which hit in November 2007, caused widespread damage and death among communities in coastal Bangladesh. A range of actions or ‘coping strategies’ were employed by households to ensure that their families had sufficient food and income resources to survive in the months following the cyclone. Coping strategies adopted by the households were categorized into three groups (i) food related, ii) income generated through selling assets, and iii) income generated though borrowing money. We found that the range of coping strategies adopted did not depend statistically on access to aquaculture assets, indicating that neither group (those with aquaculture assets and those without aquaculture assets) was better equipped to cope with the disaster. Aquaculture ponds were, however, important for supplying food and income in the post-disaster period and 78% of households were willing to re-invest in aquaculture despite the risk of stock losses and damage to infrastructure during recurrent disasters. It is concluded that aquaculture ponds are likely to provide a mechanism for coping after a disaster, despite the costs involved in repairing them. We recommend that aquaculture development be promoted for income and food security for rural families but that development occurs in areas that does not compromise other ecosystem functions i.e., mangrove forests. Risk management strategies, such as raising fast growing fish, which shortens the production cycle and allows for early harvesting, be embedded into policy reforms. We also recommend that a diversified livelihood strategy including non-farm activities be included in the reforms

Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths

OBJECTIVES: Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement.\ud \ud METHODS: Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined.\ud \ud RESULTS: Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path.\ud \ud CONCLUSIONS: Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.\ud \u

Developing a web-based SKOS editor

BACKGROUND: The Simple Knowledge Organization System (SKOS) was introduced to the wider research community by a 2005 World Wide Web Consortium (W3C) working draft, and further developed and refined in a 2009 W3C recommendation. Since then, SKOS has become the de facto standard for representing and sharing thesauri, lexicons, vocabularies, taxonomies, and classification schemes. In this paper, we describe the development of a web-based, free, open-source SKOS editor built for the development, curation, and management of small to medium-sized lexicons for health-related Natural Language Processing (NLP). RESULTS: The web-based SKOS editor allows users to create, curate, version, manage, and visualise SKOS resources. We tested the system against five widely-used, publicly-available SKOS vocabularies of various sizes and found that the editor is suitable for the development and management of small to medium-size lexicons. Qualitative testing has focussed on using the editor to develop lexical resources to drive NLP applications in two domains. First, developing a lexicon to support an Electronic Health Record-based NLP system for the automatic identification of pneumonia symptoms. Second, creating a taxonomy of lexical cues associated with Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnoses with the goal of facilitating the automatic identification of symptoms associated with depression from short, informal texts. CONCLUSIONS: The SKOS editor we have developed is - to the best of our knowledge - the first free, open-source, web-based, SKOS editor capable of creating, curating, versioning, managing, and visualising SKOS lexicons