Pain acceptance and personal control in pain relief in two maternity care models: a cross-national comparison of Belgium and the Netherlands

<p>Abstract</p> <p>Background</p> <p>A cross-national comparison of Belgian and Dutch childbearing women allows us to gain insight into the relative importance of pain acceptance and personal control in pain relief in 2 maternity care models. Although Belgium and the Netherlands are neighbouring countries sharing the same language, political system and geography, they are characterised by a different organisation of health care, particularly in maternity care. In Belgium the medical risks of childbirth are emphasised but neutralised by a strong belief in the merits of the medical model. Labour pain is perceived as a needless inconvenience easily resolved by means of pain medication. In the Netherlands the midwifery model of care defines childbirth as a normal physiological process and family event. Labour pain is perceived as an ally in the birth process.</p> <p>Methods</p> <p>Women were invited to participate in the study by independent midwives and obstetricians during antenatal visits in 2004-2005. Two questionnaires were filled out by 611 women, one at 30 weeks of pregnancy and one within the first 2 weeks after childbirth either at home or in a hospital. However, only women having a hospital birth without obstetric intervention (N = 327) were included in this analysis. A logistic regression analysis has been performed.</p> <p>Results</p> <p>Labour pain acceptance and personal control in pain relief render pain medication use during labour less likely, especially if they occur together. Apart from this general result, we also find large country differences. Dutch women with a normal hospital birth are six times less likely to use pain medication during labour, compared to their Belgian counterparts. This country difference cannot be explained by labour pain acceptance, since - in contrast to our working hypothesis - Dutch and Belgian women giving birth in a hospital setting are characterised by a similar labour pain acceptance. Our findings suggest that personal control in pain relief can partially explain the country differences in coping with labour pain. For Dutch women we find that the use of pain medication is lowest if women experience control over the reception of pain medication and have a positive attitude towards labour pain. In Belgium however, not personal control over the use of pain relief predicts the use of pain medication, but negative attitudes towards labour.</p> <p>Conclusions</p> <p>Apart from individual level determinants, such as length of labour or pain acceptance, our findings suggest that the maternity care context is of major importance in the study of the management of labour pain. The pain medication use in Belgian hospital maternity care is high and is very sensitive to negative attitudes towards labour pain. In the Netherlands, on the contrary, pain medication use is already low. This can partially be explained by a low degree of personal control in pain relief, especially when co-occurring with positive pain attitudes.</p

HDM Selection of Emerging Technology for Supercomputing Storage

Choosing to move to new technologies is a complex process, and many decisions must be made when choosing the technologies for the next supercomputer. This paper identifies key criteria for a national lab choosing storage technologies for the next generation of supercomputing and offers a model for determining one of the decisions: which emerging technology should be used for long term storage on a supercomputer for AI workloads. This paper describes these types of emerging storage technologies as well as presents a Hierarchical Decision Model (HDM) structure for choosing a storage technology strategy. The authors create a Hierarchical Decision Model (HDM) model which may be used as a basis for future storage technology decision making at various national laboratories with different workloads. This implementation of the model has been designed for a generic national laboratory, and the pairwise comparison judgments are based solely upon interviews around supercomputing and literature review about the technologies. The Storage HDM model was developed using four levels of criteria. The first level, the Mission Level, was crafted to be “Determine the emerging technologies to be pursued in the next generation of supercomputing at USA national laboratories for AI Workloads”. The second level, Objective Level, criteria were gathered from the literature review and expert opinion. The three objectives are Technological Form Factors, Economic, and Policy/Politics. To limit the scope of this project as well as keep the expert pairwise comparison data points manageable; the team focused on two criteria per objective on the third level except for Technology. The technical objective has “Reliability”, “Durability”, “Workload Bandwidth”, and “Data Storage Capacity” The Economic objective has “Production Cost” and “Productization Ability.” Finally, the Policy/Politics objective has “Drive New Technology” and “Novel Technology compared to other labs.” The last level contains the seven emerging storage technology strategies which the HDM is comparing. The seven strategies are Hybrid Cloud HPC Storage, HAMR: Heat-assisted Magnetic Recording, Helium Storage, DNA Storage, Magnetic Recording, NVM: Non-volatile Memory, and HDD. We removed Hybrid Cloud HPC Storage as it wasn’t a viable candidate for supercomputing - as it reduces the security and places compute power at the variable demands of network bandwidth. The HDM model evaluation used a single expert panel. The experts evaluated the priorities of the objectives and criteria in an aim to select the best digital storage device method. The similar platter based storage systems: HAMR, HDD, Helium, Magnetic Recording had similar outcomes. DNA data storage and NVMe came out very close as the top choices. Additional research would need to be done as DNA storage becomes a more viable and financially realistic solution

Analysis for Selecting High Risk Service Replacement Parts for Process Improvement

Currently, the majority of electronic instruments are assembled from interchangeable parts, called Field Replaceable Units or FRU. These FRU parts facilitate maintenance operations and help raise productivity. This project uses a database of FRUs and Non-FRUs to make recommendations about which parts are high risk and should have software widgets created for the parts. Another goal is to identify which of the Non-FRUs are high risk, and should be changed in the company’s inventory to become a FRU. The final goal is to identify anomalies in the data which should be investigated by the FRU lead. This project is sponsored by the FRU lead for the division. There are multiple goals of this project. The first, and most important objective of this project is to find the top FRU and Non-FRU parts with highest risk to the company. The risk is likely to be a financial concern due to a combination of high part cost, frequent failures, and high labor cost. It could also be because a part fails frequently at tier 1 customer sites, which need their tools available to run the business. Another objective is to determine high-risk Non-FRUs which should be recommended to be included as FRUs. The benefit of a part becoming a FRU is that documentation would be created on how to replace the part. There would be mandatory stocking procedures and processes in the Service Logistics and Service Management departments. The downside to the company is too many FRUs in stock can increase the cost of inventory. The cost would not be funded by support calls in the immediate future. There is also overhead to putting the FRU processes in place. The final goal is to identify anomalies in the particular part data which should be investigated by the FRU lead

Marketing Plan: REMzen

More than 70 million Americans suffer from some form of sleep disorder. REMzen is a new startup company looking to take on the burgeoning wearable market-space with an advanced sleep mask capable of precision sleep cycle monitoring, deep analytics that provide meaningful real-life coaching for better sleep, and the only sleep-cycle driven light therapy wake up on the market. REMzen benefits begin day one by monitoring your sleep and waking you up utilizing natural light therapy at the optimal moment in your sleep cycle. Wake up feeling refreshed and ready; gone are the groggy, abrupt awakenings of alarm clocks, ripping you from a deep sleep state

Cloud Service Selection for Online Fashion Retailer – HDM Analysis

Choosing to move to the cloud is a complex process, and many decisions must be made before completing the migration. This paper identifies key criteria for a company moving to the cloud and offers a model for determining one of the decisions: which cloud service strategy (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS)) should be used for a particular cloud migration project. This paper describes these types of services as well as presents a Hierarchical Decision Model (HDM) structure for choosing a service strategy

LiDAR Business Analysis

LiDAR (Light Detection and Ranging) is a remote sensing technology that uses light in the form of a laser pulses to create 3D images. LiDAR technology’s use on UAS (Unmanned Aerial System) is a relatively new concept in the rapidly advancing remote sensing & 3D imaging industry. As such, profitability data is either not released by similar business organizations, or there is simply not enough historical data to accurately predict revenue streams with either business model. To assess the feasibility of business models, market research and literature review were performed by our research team. Economic analysis methods were used to determine the most advantageous business plan strategy for the LiDAR business opportunities. Since the success of either business model will rely heavily on the company’s sales ability. Since the focus of this report is on economic analysis to make the best business decision, we have made our valuation calculations based conservative sales forecasts. The startup business plan identifies and critiques two business models identified by the founder entrepreneurs: 1. A service oriented model in which data is collected, processed, and delivered by an operations team to the customer. 2. A product oriented model in which LiDAR UAS is constructed, tested, and sold as a unit that includes training as an entire product for the customer’s own use. Based on these two models, three decision options were evaluated and compared with each other: 1. Invest in the Service only model 2. Invest in the Product only model 3. Do not invest, utilize the stock market for investment gains A 5-year cash flow analysis was conducted to give the founders a recommended strategy to optimize their investment. Through conducting a benefit cost analysis and calculating the internal rate of return of each opportunity, it was determined that both business models may be profitable and worthy investments. The expected IRR of service model and product model are 56% and 136% respectively. They are both much higher than the stock market option, which has an estimated MARR of 8% on average. Much of the risk in this analysis was the uncertainty in the sales revenue projections. The team endeavored to determine the sensitivity of the data by performing sensitivity analysis on these sales projections. Expected values were calculated taking into consideration of the most likely, optimistic and pessimistic cases. The sensitivity analysis result shows both business models are profitable under all conditions. The economic analysis has shown that while both business models are likely to be profitable, the Product Business Model has the potential for a higher return on investment. Therefore the research team recommends that the investors proceed with the Product Business Model outlined in the report