Conceptual Model Sample Clauses

Conceptual Model. 5.1 Point source waste discharges 5.2 Non-point sources of pollution 5.3 Parameters of concern 5.3.1 Environmental fate and pathways analysis for parameters of concern 5.3.2 Bioaccumulation/biomagnification risk for parameters of concern 5.4 Receptors 5.4.1 Analysis and rationale for human receptors 5.4.2 Analysis and rationale for biological receptors 5.4.3 Analysis and rationale for ecological receptors

Conceptual Model. The AIXM Conceptual Model is the component of the AIXM data standard that provides a conceptual model of aeronautical data. It models the important features, properties (attributes and associations) and business rules that make up aeronautical information. As such, it can be used as the basis for the design of an AIM database. The model is designed using the Unified Modelling Language (UML).

Conceptual Model. ‌ ESAP, in and of itself, provides no compute or analysis capabilities (beyond a simple ability to view tabular data and preview images). Rather, it acts as a broker between users and the various query and analysis services which are available to them. These might include, for example: • bulk data query systems, which can help the user locate and access data files (images, visibility data, etc) in archives, data lakes, or similar bulk storage systems; • tabular data query systems, which can help the user find relevant entries in source catalogues and similar relational systems; • Interactive Data Analysis (▇▇▇) systems, which provide the user compute and visualization tools in a convenient environment with access to relevant datasets (for example, a Jupyter [7] notebook, or con‐ tainerized analysis application); • bulk data processing systems, which provide batch (non‐interactive) processing of data at‐scale in HPC or HTC environments; • scientific software repositories, which provide access to specialist analysis tools and workflows; A given instance of ESAP is configured with information about available services1. When a user connects, the ESAP instance should: • help the user select services which are relevant to them (for example, by clearly presenting the available services; by making clear what science cases those services support, by taking account of the user’s access privileges, etc); • facilitate authentication and authorization with the various services, as necessary; • provide a consistent and convenient way for the user to access services (for example, by providing the user with a single way to enter a particular query, and then automatically translating that to the requirements of each individual service); • mediate data flow between services (for example, by enabling the user to locate data with an archive query, dispatch the data to the processing facility, and schedule processing of the data on a bulk data processing system). This relationship is illustrated schematically in Fig. 1: this shows the end user communicating directly with ESAP, which mediates their interactions with a range of other services, deployed across a variety of different infrastructures. Note that the user communicates with a single ESAP instance, while that instance mediates interactions with a range of different services from a variety of infrastructure providers.

Conceptual Model. When transmitting Data to the Data Receiver, the following means/methods/tools shall be applied: • Tools/method used • Access details e.g. authentication, credentials • Training or documentation • Etc.

Conceptual Model. This study will also use the conceptual model created ▇▇▇▇▇ ▇▇.▇▇. called “Main pathways that protect against and place wives at risk for HIV, among serodiscordant couples in Surat, India” (▇. ▇. ▇▇▇▇▇ et al., 2016) as a guide to understanding HIV risk. This model was developed using grounded theory to analyze dyadic level data to determine HIV risk among serodiscordant, married couples in Gujarat, India. This model highlights five protective and risk pathways for HIV-transmission for serodiscordant couples. The first pathway leads to safe sex, the second pathway leads to no sex, and the third pathway also leads to no sex after one of the partners either avoids or refuses sex. While risky sexual behaviors do not occur in these three pathways, safe sex or no sex can lead to unfulfilled sexual desire among either one or both partners. This unfulfilled desire could lead to extramarital sex, which increases the risk for STI acquisition and for HIV transmission to the wife. Pathway four occurs when a wife’s attempts to avoid or resist sex actually leads to coercive sex, which could also be unprotected sex. Finally, pathway five simply leads to unprotected sex. Both coerced and unprotected sex can increase HIV transmission to wives. The model also includes factors that influence the occurrence of safe sex and no sex. These factors are: positive sex communication, mutual respect and understanding, a wife’s fear of getting HIV, a wife’s assertiveness, and a husband’s desire to protect his wife from HIV. The factors that influence the occurrence of coerced sex or unprotected sex are: IPV or fear of IPV, a husband’s alcohol use, condom displeasure, and the desire for children. This conceptual model nicely complements the TGP because it takes the risk factors highlighted by the theory and maps them to the actual risky behaviors. It allows for a complete understanding of risk factors and risky behaviors specifically among serodiscordant married couples in Gujarat. See Figure 1 for the full conceptual model. This study used both the TGP and this conceptual model to understand HIV risk factors for serodiscordant couples in Gujarat. Based on the information presented above, this study aimed to answer the following:

Conceptual Model. While we have included urbanicity as a construct in this model, it is important to understand that nearly all retail clinics are located in urban areas. As such, the relationships among the other constructs are presented for urban populations and not the population as a whole.

Conceptual Model. Variables

Conceptual Model. The present analysis was based on the CAM Healthcare Model20 which is a modification of ▇▇▇▇▇▇▇▇’▇ Behavioral Model for Health Services Use21, 22. According to the CAM Healthcare Model, CAM use is dictated by both “push” and “pull” related factors. Factors that may “push” an individual away from conventional care include dissatisfaction with conventional care and financial issues (e.g., cost of care, low income, lack of health insurance); factors that may “pull” an individual towards CAM may include personal values that prioritize self-care and positive beliefs about CAM being “natural.” These individual-level determinants of CAM use can be classified into three main categories: predisposing factors, enabling factors and need-based factors. According to the model, predisposing factors are those that influence whether an individual will use CAM. These factors are divided into demographic characteristics (e.g., gender, age and marital status), social structure (e.g., education), beliefs and values (e.g., satisfaction with conventional healthcare) and personal factors (e.g., perceived self- efficacy and perceived control over health/healthcare). Enabling factors (resources) are those that either facilitate or impede an individual’s use of CAM. For example, individuals must have the financial means (e.g., income and employment) and the “know- how” to access CAM (e.g., health literacy). Finally, need-based factors refer to an individual’s health status or illness state and are divided into perceived need factors and evaluated need factors. Perceived need factors are subjective assessments of health status (e.g., perceived symptom severity, self-reported quality of life, etc.); evaluated need factors include those that are based on objective assessments of disease status (e.g., date of diagnosis, number of doctor’s office visits, etc.). Using these categories, the model aims to identify factors associated with CAM use and enhance understanding of factors that predict CAM use20. Though the present study examined whether these individual-level determinants predicted CAM use, it is important to note that the model also accounts for the potential impact of social and system-based factors on CAM use such as changes in the availability of CAM therapies in conventional healthcare settings, availability of CAM-related training in schools of medicine, nursing, pharmacy and public health and health insurance reimbursement policies for CAM therapy utilization.

Conceptual Model. HRM policies and business performance Hypothesis 1: The calculative HRM policy is positively related to firm performance. Hypothesis 2: The collective sharing HRM policy is positively related to firm performance. ▇▇▇▇ ▇▇▇▇▇▇▇, ▇▇▇▇ ▇.▇ ▇▇▇▇▇▇▇▇ and ▇▇▇▇ ▇▇▇▇▇ 83 Hypothesis 3: The collaborative HRM policy is positively related to firm performance. Hypothesis 4: The positive relationship of a calculative HRM policy with firm performance is stronger for organizations that have a high collaborative HRM policy than for organizations that have a low collaborative HRM policy.