Horizontal Violence in Nursing - 1512 Words

The High Cost of Horizontal Violence in Nursing Paisley Rojo Gen 200 July 21, 2015 Dr. Davidov The High Cost of Horizontal Violence In nursing, there is a growing concern called, horizontal violence. Horizontal violence is an act of aggression from one nurse to the other. Horizontal violence impedes teamwork, hinders patient care and causes a negative work environment (Becher visovsky, 2012). Even though workplace politics exists in every profession, the effects of horizontal violence, or bullying, in nursing is a costly behavior. Nurses feel devalued in the workplace and experience psychological effects. Patients are likely to experience less favorable outcomes, and retention is difficult costing facilities†¦show more content†¦If this behavior is a problem for the clinical facility, that facility will likely have a higher turnover rate, which will in turn cost more money in recruitment and education. Moreover, bullying also affects continuity of care with patients and may result in sentinel events as well as legal action against the clinician, and facility from pat ients and family members. According to Aleccia (2008) an estimated 70 percent of nurses involved in a study of horizontal violence believed there was a relation to inappropriate behavior from peers and negative outcomes for patients. Moreover, 25 percent of the study group believed that horizontal violence contributed to patient mortality. Research has shown that there is an estimated $30,000 to $100,000 per annum paid for each victim of horizontal violence. This cost stems from absenteeism, treatment of the psychological effects, and the high rate of turnover (Gerardi Connell, 2007). Woelfle and McCaffrey (2007) investigated the damage caused by horizontal violence to work relationships in nursing to find a solution. The result of that investigation stated that horizontal violence in nursing is commonplace and experienced by nursing students, nurses with limited experience, and seasoned nurses equally. Solutions According to the American Nurses Association, currently federal standards do not require protection from violence in the workplace. Some states have askedShow MoreRelatedHorizontal Violence And Its Effects On Nursing1656 Words   |  7 PagesHorizontal Violence: A Detriment to Nursing Typically, when someone thinks of a bully, childhood memories of scuffles on the playground come to mind. Bullies are not usually people that are associated with adult life. However, nursing has changed this stereotypical view. For many nurses, bullying may be as great a threat every day at work as it was when they were in grade school. This threat is because of what is termed as horizontal or lateral violence in the workplace, and it is a surprisinglyRead MoreHorizontal Violence And Its Effects On Nursing1942 Words   |  8 PagesHorizontal Violence: A Detriment to Nursing Typically, when someone thinks of a bully, childhood memories of scuffles on the playground come to mind. Bullies are not usually people that are associated with adult life. However, nursing has changed this stereotypical view. For many nurses, bullying may be as great a threat every day at work as it was when they were in grade school. This threat is because of what is termed as horizontal or lateral violence in the workplace, and it is a surprisinglyRead MoreHorizontal Violence and Staff Morale Essay1049 Words   |  5 PagesWhen one goes into the profession of nursing it is usually not for the money, job security or the glamour of the job. Those of us who chose nursing as our profession generally care for others and want to make a difference in the world. New nurses are excited to become licensed and to start working in the profession of nursing. When new nurses enter the field of nursing, they are often met with more experienced nurses who have practiced longer and subseq uently has more experience. This maybe anRead MoreHorizontal violence and The Effects on Nurses and Patients Essay1234 Words   |  5 Pages Horizontal violence is not a topic that medical faculties discuss on a day-to-day basis, but it is an enormous problem within the health care system. In this research the author looks at bulling from a registered nurse (r.n.) aspect .The effects on patient centered care can be detrimental for patients and r.n.’s. The work place needs to be a safe place for not only the patients but also the employees. With the rise of new graduate nurses who are employed by the medical facilities, they too areRead MoreThe Effect Of Incivility On Nursing Turnover And Patient Care1640 Words   |  7 PagesThe Effect of Incivility on Nursing Turnover and Patient Care Incivility is an umbrella term used to describe any type of negative behavior directed towards another individual that may impact the way that they behave and/or feel. While incidences of incivility may be visible in most professional careers, it’s significance in nursing has prompted a provisional statement from the American Nurses Association. â€Å"The nurse creates an ethical environment and culture of civility and kindness. Disregard forRead MoreWorkplace Is A Comprehensive Issue Is Not Only Affects A Person s Dignity Essay1458 Words   |  6 PagesISSUES Workplace bullying is a comprehensive issue which not only affects a person’s dignity, but also causes psychological and emotional problems (Park, Cho Hong, 2015). The results of horizontal violence will be job dissatisfaction, physical and psychological stress. The recipient’s of HV will be having sleeping difficulties, low self-confidence and low enthusiasm, feeling isolated from other staff, show depression and utilize lots of sick leave (Longo Sherman, 2007). On a study conducted onRead MoreLateral Violence in the Workplace1270 Words   |  6 PagesLateral violence in the workplace Lona A Smeltzer Southern New Hampshire University Lateral violence in the workplace Abstract This paper explores five published articles as they relate to the concept of Lateral violence (LV) within the nursing profession and how it directly affects the work environment. The concept of LV is also known as abusive behavior, horizontal violence, bullying, aggression, horizontal hostility, verbal abuse or â€Å"nurses eating their young†. There are fourRead MoreNursing Research Study Summary Essay1096 Words   |  5 PagesNursing Research Study Summary There are six levels of violence identified among registered nurses including psychological, physical, vertical, horizontal, covert, and overt. The article research by this author was found in the Nursing Forum Journal, titled Levels of violence among nurses in Cape Town public hospitals (Khalil, 2009). Nursing research was done to identify and understand why the nursing profession lets violence happen among nurses within the profession. Background Violence amongRead MoreDoctor Of Nursing Practice Roles1434 Words   |  6 PagesDoctor of Nursing Practice Roles against Workplace Violence in the Nursing Profession Introduction Workplace violence in nursing has become a serious problem that affects the nursing profession in many aspects (Murray, 2009). According to Skehan (2015), there were few studies or documentation about the perception or management of nurse leaders against workplace violence in nursing. Therefore, doctor of nursing practice (DNP) prepared nurses should become the experts in identifying, analyzing, andRead MoreVertical Violence And Its Effects On The Nursing Profession1107 Words   |  5 PagesNursing is a profession that is based on the principles of caring and ethics. For years nursing has been continuously ranked as the most trusted profession (Riffkin, 2014). Nurses advocate for patients even if it means standing up to doctors and hospital administrators. Shockingly, for a job rooted in compassion, horizontal violence is a devastatingly common occurrence in the nursing profession. Horizontal violence, or workplace bullying, is defined as repeated incidences of aggressive behaviour

Walmart Security Issues Free Essays

string(61) " finding a needle in the haystack, they are making more hay\." Review our  cookies information  for more details Special report:  Managing information A different game Information is transforming traditional businesses Feb 25th 2010 | from the print edition * * IN 1879 James Ritty, a saloon-keeper in Dayton, Ohio, received a patent for a wooden contraption that he dubbed the â€Å"incorruptible cashier†. With a set of buttons and a loud bell, the device, sold by National Cash Register (NCR), was little more than a simple adding machine. Yet as an early form of managing information flows in American business the cash register had a huge impact. We will write a custom essay sample on Walmart Security Issues or any similar topic only for you Order Now It not only reduced pilferage by alerting the shopkeeper when the till was opened; by recording every transaction, it also provided an instant overview of what was happening in the business. Sales data remain one of a company’s most important assets. In 2004 Wal-Mart peered into its mammoth databases and noticed that before a hurricane struck, there was a run on flashlights and batteries, as might be expected; but also on Pop-Tarts, a sugary American breakfast snack. On reflection it is clear that the snack would be a handy thing to eat in a blackout, but the retailer would not have thought to stock up on it before a storm. The company whose system crunched Wal-Mart’s numbers was none other than NCR and its data-warehousing unit, Teradata, now an independent firm. A few years ago such technologies, called â€Å"business intelligence†, were available only to the world’s biggest companies. But as the price of computing and storage has fallen and the software systems have got better and cheaper, the technology has moved into the mainstream. Companies are collecting more data than ever before. In the past they were kept in different systems that were unable to talk to each other, such as finance, human resources or customer management. Now the systems are being linked, and companies are using data-mining techniques to get a complete picture of their operations—â€Å"a single version of the truth†, as the industry likes to call it. That allows firms to operate more efficiently, pick out trends and improve their forecasting. In this special report * Data, data everywhere * All too much *  »A different game * Clicking for gold * The open society * Show me * Needle in a haystack * New rules for big data * Handling the cornucopia Sources acknowledgementsReprints Related topics * China * Nestle * IBM * Royal Shakespeare Company * Walmart Consider Cablecom, a Swiss telecoms operator. It has reduced customer defections from one-fifth of subscribers a year to under 5% by crunching its numbers. Its software spotted that although customer defections peaked in the 13th month, the decision to leave was made much earlier, around the ninth month (as indicated by things like the number of calls to customer support services). So Cablecom offered certain customers special deals seven months into their subscription and reaped the rewards. Agony and torture Such data-mining has a dubious reputation. â€Å"Torture the data long enough and they will confess to anything,† statisticians quip. But it has become far more effective as more companies have started to use the technology. Best Buy, a retailer, found that 7% of its customers accounted for 43% of its sales, so it reorganised its stores to concentrate on those customers’ needs. Airline yield management improved because analytical techniques uncovered the best predictor that a passenger would actually catch a flight he had booked: that he had ordered a vegetarian meal. The IT industry is piling into business intelligence, seeing it as a natural successor of services such as accountancy and computing in the first and second half of the 20th century respectively. Accenture, PricewaterhouseCoopers, IBM and SAP are investing heavily in their consulting practices. Technology vendors such as Oracle, Informatica, TIBCO, SAS and EMC have benefited. IBM believes business intelligence will be a pillar of its growth as sensors are used to manage things from a city’s traffic flow to a patient’s blood flow. It has invested $12 billion in the past four years and is opening six analytics centres with 4,000 employees worldwide. Analytics—performing statistical operations for forecasting or uncovering correlations such as between Pop-Tarts and hurricanes—can have a big pay-off. In Britain the Royal Shakespeare Company (RSC) sifted through seven years of sales data for a marketing campaign that increased regular visitors by 70%. By examining more than 2m transaction records, the RSC discovered a lot more about its best customers: not just income, but things like occupation and family status, which allowed it to target its marketing more precisely. That was of crucial importance, says the RSC’s Mary Butlin, because it substantially boosted membership as well as fund-raising revenue. Yet making the most of data is not easy. The first step is to improve the accuracy of the information. Nestle, for example, sells more than 100,000 products in 200 countries, using 550,000 suppliers, but it was not using its huge buying power effectively because its databases were a mess. On examination, it found that of its 9m records of vendors, customers and materials around half were obsolete or duplicated, and of the remainder about one-third were inaccurate or incomplete. The name of a vendor might be abbreviated in one record but spelled out in another, leading to double-counting. Plainer vanilla Over the past ten years Nestle has been overhauling its IT system, using SAP software, and improving the quality of its data. This enabled the firm to become more efficient, says Chris Johnson, who led the initiative. For just one ingredient, vanilla, its American operation was able to reduce the number of specifications and use fewer suppliers, saving $30m a year. Overall, such operational improvements save more than $1 billion annually. Nestle is not alone in having problems with its database. Most CIOs admit that their data are of poor quality. In a study by IBM half the managers quizzed did not trust the information on which they had to base decisions. Many say that the technology meant to make sense of it often just produces more data. Instead of finding a needle in the haystack, they are making more hay. You read "Walmart Security Issues" in category "Essay examples" Still, as analytical techniques become more widespread, business decisions will increasingly be made, or at least corroborated, on the basis of computer algorithms rather than individual hunches. This creates a need for managers who are comfortable with data, but statistics courses in business schools are not popular. Many new business insights come from â€Å"dead data†: stored information about past transactions that are examined to reveal hidden correlations. But now companies are increasingly moving to analysing real-time information flows. Wal-Mart is a good example. The retailer operates 8,400 stores worldwide, has more than 2m employees and handles over 200m customer transactions each week. Its revenue last year, around $400 billion, is more than the GDP of many entire countries. The sheer scale of the data is a challenge, admits Rollin Ford, the CIO at Wal-Mart’s headquarters in Bentonville, Arkansas. â€Å"We keep a healthy paranoia. † Not a sparrow falls Wal-Mart’s inventory-management system, called Retail Link, enables suppliers to see the exact number of their products on every shelf of every store at that precise moment. The system shows the rate of sales by the hour, by the day, over the past year and more. Begun in the 1990s, Retail Link gives suppliers a complete overview of when and how their products are selling, and with what other products in the shopping cart. This lets suppliers manage their stocks better. The technology enabled Wal-Mart to change the business model of retailing. In some cases it leaves stock management in the hands of its suppliers and does not take ownership of the products until the moment they are sold. This allows it to shed inventory risk and reduce its costs. In essence, the shelves in its shops are a highly efficiently managed depot. Another company that capitalises on real-time information flows is Li Fung, one of the world’s biggest supply-chain operators. Founded in Guangzhou in southern China a century ago, it does not own any factories or equipment but orchestrates a network of 12,000 suppliers in 40 countries, sourcing goods for brands ranging from Kate Spade to Walt Disney. Its turnover in 2008 was $14 billion. Li ; Fung used to deal with its clients mostly by phone and fax, with e-mail counting as high technology. But thanks to a new web-services platform, its processes have speeded up. Orders flow through a web portal and bids can be solicited from pre-qualified suppliers. Agents now audit factories in real time with hand-held computers. Clients are able to monitor the details of every stage of an order, from the initial production run to shipping. One of the most important technologies has turned out to be videoconferencing. It allows buyers and manufacturers to examine the colour of a material or the stitching on a garment. â€Å"Before, we weren’t able to send a 500MB image—we’d post a DVD. Now we can stream it to show vendors in our offices. With real-time images we can make changes quicker,† says Manuel Fernandez, Li ; Fung’s chief technology officer. Data flowing through its network soared from 100 gigabytes a day only 18 months ago to 1 terabyte. The information system also allows Li Fung to look across its operations to identify trends. In southern China, for instance, a shortage of workers and new legislation raised labour costs, so production moved north. â€Å"We saw that before it actually happened,† says Mr Fernandez. The company also got advance warning of the economic crisis, and later the recovery, from retailers’ orders before these trends became apparent. Investment analysts use country information provided by Li ; Fung to gain insights into macroeconomic patterns. Now that they are able to process information flows in real time, organisations are collecting more data than ever. One use for such information is to forecast when machines will break down. This hardly ever happens out of the blue: there are usually warning signs such as noise, vibration or heat. Capturing such data enables firms to act before a breakdown. Similarly, the use of â€Å"predictive analytics† on the basis of large data sets may transform health care. Dr Carolyn McGregor of the University of Ontario, working with IBM, conducts research to spot potentially fatal infections in premature babies. The system monitors subtle changes in seven streams of real-time data, such as respiration, heart rate and blood pressure. The electrocardiogram alone generates 1,000 readings per second. This kind of information is turned out by all medical equipment, but it used to be recorded on paper and examined perhaps once an hour. By feeding the data into a computer, Dr McGregor has been able to detect the onset of an infection before obvious symptoms emerge. â€Å"You can’t see it with the naked eye, but a computer can,† she says. Open sesame Two technology trends are helping to fuel these new uses of data: cloud computing and open-source software. Cloud computing—in which the internet is used as a platform to collect, store and process data—allows businesses to lease computing power as and when they need it, rather than having to buy expensive equipment. Amazon, Google and Microsoft are the most prominent firms to make their massive computing infrastructure available to clients. As more corporate functions, such as human resources or sales, are managed over a network, companies can see patterns across the whole of the business and share their information more easily. A free programming language called R lets companies examine and present big data sets, and free software called Hadoop now allows ordinary PCs to analyse huge quantities of data that previously required a supercomputer. It does this by parcelling out the tasks to numerous computers at once. This saves time and money. For example, the  New York Times  a few years ago used cloud computing and Hadoop to convert over 400,000 scanned images from its archives, from 1851 to 1922. By harnessing the power of hundreds of computers, it was able to do the job in 36 hours. Visa, a credit-card company, in a recent trial with Hadoop crunched two years of test records, or 73 billion transactions, amounting to 36 terabytes of data. The processing time fell from one month with traditional methods to a mere 13 minutes. It is a striking successor of Ritty’s incorruptible cashier for a data-driven age. from the print edition | Special report Recommend 140 * * * Submit to reddit * inShare2 * View all comments (4) Related items TOPIC:  China  Ã‚ » * Recommended economics writing: Link exchange * Trade: Mexico rising * The Economist: Digital highlights, November 24th 2012 TOPIC:  Nestle  Ã‚ » * Consumer goods in Africa: A continent goes shopping * Schumpeter: Pretty profitable parrots * Nestle buys Pf izer Nutrition: Feeding little emperors TOPIC:  IBM  Ã‚ » * Schumpeter: Taking the long view * IBM’s mainframes: Old dog, new tricks * Phase-change memory: Altered states TOPIC:  Royal Shakespeare Company  Ã‚ » * William Shakespeare: A digital reinvention Culture: Going for gold * Green architecture: The retrofit revolution More related topics: * Walmart Want more? 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By continuing to browse the site you are agreeing to our use of cookies. Review our  cookies information  for more details Special report:  Managing information A different game Information is transforming traditional businesses Feb 25th 2010 | from the print edition * * IN 1879 James Ritty, a saloon-keeper in Dayton, Ohio, received a patent for a wooden contraption that he dubbed the â€Å"incorruptible cashier†. With a set of buttons and a loud bell, the device, sold by National Cash Register (NCR), was little more than a simple adding machine. Yet as an early form of managing information flows in American business the cash register had a huge impact. It not only reduced pilferage by alerting the shopkeeper when the till was opened; by recording every transaction, it also provided an instant overview of what was happening in the business. Sales data remain one of a company’s most important assets. In 2004 Wal-Mart peered into its mammoth databases and noticed that before a hurricane struck, there was a run on flashlights and batteries, as might be expected; but also on Pop-Tarts, a sugary American breakfast snack. On reflection it is clear that the snack would be a handy thing to eat in a blackout, but the retailer would not have thought to stock up on it before a storm. The company whose system crunched Wal-Mart’s numbers was none other than NCR and its data-warehousing unit, Teradata, now an independent firm. A few years ago such technologies, called â€Å"business intelligence†, were available only to the world’s biggest companies. But as the price of computing and storage has fallen and the software systems have got better and cheaper, the technology has moved into the mainstream. Companies are collecting more data than ever before. In the past they were kept in different systems that were unable to talk to each other, such as finance, human resources or customer management. Now the systems are being linked, and companies are using data-mining techniques to get a complete picture of their operations—â€Å"a single version of the truth†, as the industry likes to call it. That allows firms to operate more efficiently, pick out trends and improve their forecasting. In this special report * Data, data everywhere * All too much *  »A different game * Clicking for gold * The open society * Show me * Needle in a haystack * New rules for big data * Handling the cornucopia Sources acknowledgementsReprints Related topics * China * Nestle * IBM * Royal Shakespeare Company * Walmart Consider Cablecom, a Swiss telecoms operator. It has reduced customer defections from one-fifth of subscribers a year to under 5% by crunching its numbers. Its software spotted that although customer defections peaked in the 13th month, the decision to leave was made much earlier, around the ninth month (as indicated by things like the number of calls to customer support services). So Cablecom offered certain customers special deals seven months into their subscription and reaped the rewards. Agony and torture Such data-mining has a dubious reputation. â€Å"Torture the data long enough and they will confess to anything,† statisticians quip. But it has become far more effective as more companies have started to use the technology. Best Buy, a retailer, found that 7% of its customers accounted for 43% of its sales, so it reorganised its stores to concentrate on those customers’ needs. Airline yield management improved because analytical techniques uncovered the best predictor that a passenger would actually catch a flight he had booked: that he had ordered a vegetarian meal. The IT industry is piling into business intelligence, seeing it as a natural successor of services such as accountancy and computing in the first and second half of the 20th century respectively. Accenture, PricewaterhouseCoopers, IBM and SAP are investing heavily in their consulting practices. Technology vendors such as Oracle, Informatica, TIBCO, SAS and EMC have benefited. IBM believes business intelligence will be a pillar of its growth as sensors are used to manage things from a city’s traffic flow to a patient’s blood flow. It has invested $12 billion in the past four years and is opening six analytics centres with 4,000 employees worldwide. Analytics—performing statistical operations for forecasting or uncovering correlations such as between Pop-Tarts and hurricanes—can have a big pay-off. In Britain the Royal Shakespeare Company (RSC) sifted through seven years of sales data for a marketing campaign that increased regular visitors by 70%. By examining more than 2m transaction records, the RSC discovered a lot more about its best customers: not just income, but things like occupation and family status, which allowed it to target its marketing more precisely. That was of crucial importance, says the RSC’s Mary Butlin, because it substantially boosted membership as well as fund-raising revenue. Yet making the most of data is not easy. The first step is to improve the accuracy of the information. Nestle, for example, sells more than 100,000 products in 200 countries, using 550,000 suppliers, but it was not using its huge buying power effectively because its databases were a mess. On examination, it found that of its 9m records of vendors, customers and materials around half were obsolete or duplicated, and of the remainder about one-third were inaccurate or incomplete. The name of a vendor might be abbreviated in one record but spelled out in another, leading to double-counting. Plainer vanilla Over the past ten years Nestle has been overhauling its IT system, using SAP software, and improving the quality of its data. This enabled the firm to become more efficient, says Chris Johnson, who led the initiative. For just one ingredient, vanilla, its American operation was able to reduce the number of specifications and use fewer suppliers, saving $30m a year. Overall, such operational improvements save more than $1 billion annually. Nestle is not alone in having problems with its database. Most CIOs admit that their data are of poor quality. In a study by IBM half the managers quizzed did not trust the information on which they had to base decisions. Many say that the technology meant to make sense of it often just produces more data. Instead of finding a needle in the haystack, they are making more hay. Still, as analytical techniques become more widespread, business decisions will increasingly be made, or at least corroborated, on the basis of computer algorithms rather than individual hunches. This creates a need for managers who are comfortable with data, but statistics courses in business schools are not popular. Many new business insights come from â€Å"dead data†: stored information about past transactions that are examined to reveal hidden correlations. But now companies are increasingly moving to analysing real-time information flows. Wal-Mart is a good example. The retailer operates 8,400 stores worldwide, has more than 2m employees and handles over 200m customer transactions each week. Its revenue last year, around $400 billion, is more than the GDP of many entire countries. The sheer scale of the data is a challenge, admits Rollin Ford, the CIO at Wal-Mart’s headquarters in Bentonville, Arkansas. â€Å"We keep a healthy paranoia. † Not a sparrow falls Wal-Mart’s inventory-management system, called Retail Link, enables suppliers to see the exact number of their products on every shelf of every store at that precise moment. The system shows the rate of sales by the hour, by the day, over the past year and more. Begun in the 1990s, Retail Link gives suppliers a complete overview of when and how their products are selling, and with what other products in the shopping cart. This lets suppliers manage their stocks better. The technology enabled Wal-Mart to change the business model of retailing. In some cases it leaves stock management in the hands of its suppliers and does not take ownership of the products until the moment they are sold. This allows it to shed inventory risk and reduce its costs. In essence, the shelves in its shops are a highly efficiently managed depot. Another company that capitalises on real-time information flows is Li Fung, one of the world’s biggest supply-chain operators. Founded in Guangzhou in southern China a century ago, it does not own any factories or equipment but orchestrates a network of 12,000 suppliers in 40 countries, sourcing goods for brands ranging from Kate Spade to Walt Disney. Its turnover in 2008 was $14 billion. Li ; Fung used to deal with its clients mostly by phone and fax, with e-mail counting as high technology. But thanks to a new web-services platform, its processes have speeded up. Orders flow through a web portal and bids can be solicited from pre-qualified suppliers. Agents now audit factories in real time with hand-held computers. Clients are able to monitor the details of every stage of an order, from the initial production run to shipping. One of the most important technologies has turned out to be videoconferencing. It allows buyers and manufacturers to examine the colour of a material or the stitching on a garment. â€Å"Before, we weren’t able to send a 500MB image—we’d post a DVD. Now we can stream it to show vendors in our offices. With real-time images we can make changes quicker,† says Manuel Fernandez, Li ; Fung’s chief technology officer. Data flowing through its network soared from 100 gigabytes a day only 18 months ago to 1 terabyte. The information system also allows Li Fung to look across its operations to identify trends. In southern China, for instance, a shortage of workers and new legislation raised labour costs, so production moved north. â€Å"We saw that before it actually happened,† says Mr Fernandez. The company also got advance warning of the economic crisis, and later the recovery, from retailers’ orders before these trends became apparent. Investment analysts use country information provided by Li ; Fung to gain insights into macroeconomic patterns. Now that they are able to process information flows in real time, organisations are collecting more data than ever. One use for such information is to forecast when machines will break down. This hardly ever happens out of the blue: there are usually warning signs such as noise, vibration or heat. Capturing such data enables firms to act before a breakdown. Similarly, the use of â€Å"predictive analytics† on the basis of large data sets may transform health care. Dr Carolyn McGregor of the University of Ontario, working with IBM, conducts research to spot potentially fatal infections in premature babies. The system monitors subtle changes in seven streams of real-time data, such as respiration, heart rate and blood pressure. The electrocardiogram alone generates 1,000 readings per second. This kind of information is turned out by all medical equipment, but it used to be recorded on paper and examined perhaps once an hour. By feeding the data into a computer, Dr McGregor has been able to detect the onset of an infection before obvious symptoms emerge. â€Å"You can’t see it with the naked eye, but a computer can,† she says. Open sesame Two technology trends are helping to fuel these new uses of data: cloud computing and open-source software. Cloud computing—in which the internet is used as a platform to collect, store and process data—allows businesses to lease computing power as and when they need it, rather than having to buy expensive equipment. Amazon, Google and Microsoft are the most prominent firms to make their massive computing infrastructure available to clients. As more corporate functions, such as human resources or sales, are managed over a network, companies can see patterns across the whole of the business and share their information more easily. A free programming language called R lets companies examine and present big data sets, and free software called Hadoop now allows ordinary PCs to analyse huge quantities of data that previously required a supercomputer. It does this by parcelling out the tasks to numerous computers at once. This saves time and money. For example, the  New York Times  a few years ago used cloud computing and Hadoop to convert over 400,000 scanned images from its archives, from 1851 to 1922. By harnessing the power of hundreds of computers, it was able to do the job in 36 hours. Visa, a credit-card company, in a recent trial with Hadoop crunched two years of test records, or 73 billion transactions, amounting to 36 terabytes of data. The processing time fell from one month with traditional methods to a mere 13 minutes. It is a striking successor of Ritty’s incorruptible cashier for a data-driven age. rom the print edition | Special report * Recommend 140 * * * Submit to reddit * inShare2 * View all comments (4) Related items TOPIC:  China  Ã‚ » * Recommended economics writing: Link exchange * Trade: Mexico rising * The Economist: Digital highlights, November 24th 2012 TOPIC:  Nestle  Ã‚ » * Consumer goods in Africa: A continent goes shopping * Schumpeter: Pretty profita ble parrots * Nestle buys Pfizer Nutrition: Feeding little emperors TOPIC:  IBM  Ã‚ » * Schumpeter: Taking the long view * IBM’s mainframes: Old dog, new tricks * Phase-change memory: Altered states TOPIC:  Royal Shakespeare Company  Ã‚ » William Shakespeare: A digital reinvention * Culture: Going for gold * Green architecture: The retrofit revolution More related topics: * Walmart Want more? Subscribe to  The Economist  and get the week’s most relevant news and analysis. * Print edition X Feb 27th 2010 Feb 20th 2010 Feb 13th 2010 Feb 6th 2010 * Next in The world this week X Politics this week * Next in The world this week X Business this week * Next in The world this week X KAL’s cartoon * Next in Leaders X Technology The data deluge Businesses, governments and society are only starting to tap its vast potential * Next in Leaders X Argentina and the Falklands The beef in Buenos Aires The Kirchners could have more oil if they stopped bullying Argentine business * Next in Leaders X Japan’s frustrating politics Nagasaki fallout Japan’s prime minister, Yukio Hatoyama, should jettison his Svengali, Ichiro Ozawa * Next in Leaders X India Ending the red terror It is time India got serious about the Maoist insurgency in its eastern states * Next in Leaders X Genetically modified food Attack of the really quite likeable tomatoes The success of genetically modified crops provides opportunities to win over their critics Next in Letters X Letters On Spain, al-Qaeda, Yemen, torture, Britain, juries, stereotypes, Benjamin Disraeli * Next in Briefing X Argentina under the Kirchners Socialism for foes, capitalism for friends While some private businesses in Argentina have faced harassment or even nationalisation, others†¦ * Next in Briefing X The first family’s businesses Welcome to the Hotel Kirchn er Such a lovely little earner * Next in United States X Health reform Seizing the reins, at long last After leaving Congress in charge for too long, Barack Obama unveils his own plan * Next in United States X Mitt Romney and the Republicans Fired up, ready to go Mitt Romney takes centre-stage * Next in United States X The administration’s economists Grading the dismal scientists How good is the Council of Economic Advisers? * Next in United States X The economy Back to the crash The American economy has just had its worst decade since the 1930s * Next in United States X Arkansas politics Democrats beware A spirited scramble for suddenly open Democratic seats * Next in United States X Schools and testing The finger of suspicion Is too much weight given to testing? * Next in United States X California’s prison-guards’ union Fading are the peacemakers One of California’s most powerful political forces may have peaked * Next in United States X America’s children Protecting the weakest The recession may hurt America’s vulnerable children * Next in United States X Lexington Is Barack Obama tough enough? Conservatives call him too weak to be a warrior. Tell that to the Taliban * Next in The Americas X Corruption in Brazil The money trail Many corruption scandals stem from the high cost of politics, and unrealistically tight†¦ * Next in The Americas X Presidential ambitions in Peru Political satire Jaime Bayly’s breath of fresh air * Next in The Americas X Latin American summitry In ever-closer union, divided we stand * Next in The Americas X Canada’s Mohawks Get out of our canoe When a Canadian is not a Canadian * Next in Asia X Tackling Japan’s bureaucracy Floundering in the foggy fortress The DPJ is finding that it needs to befriend its bureaucrats, as well as bash them * Next in Asia X India’s Naxalite insurgency Not a dinner party India’s Maoist guerrillas carry out two slaughters, then offer a truce * Next in Asia X Western aims in Afghanistan Played for fools Hamid Karzai’s shenanigans make the going even harder for NATO * Next in Asia X Migrant workers in Thailand Inhospitality Life gets harder for Thailand’s guest-workers * Next in Asia X China’s National People’s Congress Democracy in action Making sure that China’s supreme legislative body is toothless * Next in Asia X Animal welfare in China Off the menu The right to eat cats and dogs is under threat * Next in Asia X Banyan The mother of all dictatorships To understand North Korea, look not to Confucius or the Soviet Union, but to fascist 1930s Japan * Next in Asia X How to cite Walmart Security Issues, Essay examples

Psychology Reflective free essay sample

After having various lessons, I would like to have a deeper evaluation of the chapter â€Å"Behavior in Social and Cultural Context† especially the concept of attributions . It is known that there are two types of attributions which are internal attributions and external attributions that we generally use to explain our own or other’s behaviors. Internal factors concern a person’s traits while external factors concern the external environment. In addition, I am actually shocked by the fact that there is a fundamental attribution error when we are explaining others’ behaviors. There is a real-life example that I would like to share. Last Monday, I was stuck in a traffic jam for half an hour on Nathan Road. I had a lesson at 8:30 am in Core A and I reached the pedestrian bridge at 8:25 am, so I was rushing to the classroom. At that moment, a scene annoyed me most and stopped my way to school. We will write a custom essay sample on Psychology Reflective or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page A boy who was around six years old was too energetic. He dashed and rushed around on the footbridge that disturbed others’ way. He also guffawed and touched or played with anything and everything he saw. Suddenly, he paced around and glared at his mum. Don’t walk like a stupid pig! Do you know how to walk? I have been waiting for you for so long! †He shouted at his mum. At that moment, I was very angry and strongly believed that the boy was so naughty and disrespectful that he showed his emotions with no restraint and did whatever he likes without regard for consequences. Based on the above case, the correspondence bias leads me to explain the boy’s behavior by ignoring the influence of situation on behavior. For example, actually he is a child with attention deficit hyperactivity disorder, so he cannot be patient and always dash around. However, we tend to emphasize the dispositional attribution that the boy is so naughty and disrespectful. Apparently, we tend to overestimate internal factors and underestimate external factors when explaining others’ behavior. After understanding the concept of fundamental attribution error, I have an enquiry related to it. Is there an error too when we explain our own behaviors? In order to find out the answer of it, I do some researches on it. Afterwards, I found out that the concept of actor-observer bias which is proposed by E. E. Jones and R. E. Nisbett in 1971 gives a clearer picture on the error of explaining our own and others’ behaviors. It states that we as an actor are more likely to attribute our own actions to the particular situation than to a generalization about our personality while the reverse asymmetry held for people being an observer and explaining others’ behaviors. Nevertheless, I have doubts about both the fundamental attribution error and the actor-observer bias. In my opinion, I think that both of the ideas only firmly established when describing negative events. For instance, on the one hand, as an actor, when we get bad result on an exam, we usually attribute the reason to the difficult exam (situational). On the other hand, as an observer, when our friends get bad academic result, we usually attribute the reason to his or her lazy character (dispositional). If the event is positive, the reverse error occurs. With the same example but with the condition that both we and our friends get high marks in the exam, we will attribute the reason to hard-working (dispositional) and easy exam (situational) to explain behaviors respectively. Malle (2006) agrees that a reverse asymmetry held for positive events after conducting a mental-analysis. He states that the discrepancy may indicate a self-serving pattern in attribution that we attribute success to internal factors and failure to external factors. Therefore, I believe that we are explaining others’ by using both the self-serving bias and fundamental attribution error. Overall, the lessons build up my foundation for the psychological concepts and theories and we need to explore the psychological world by ourselves in order to find out more details, conflicts about and relationships between different ideas. For instance, in order to finish this reflective journal, I used Google scholar to search about the actor-observer bias and the criticisms about it that I did not learn on the book and in lessons. By experiencing the searching process, I have deeper understanding on it and it strongly impresses on my memory. The process also enhances my analysis skill, to determine which sources are useful and which sources are not related to my topic. Therefore, I enjoy the process of exploring the psychological knowledge by ourselves.