Nurses are the health care professionals that collect and prepare medications for patients. They examine the doctor’s orders to see what medications patients are prescribed. Errors can occur in the distribution of these medications. As a result‚ the nursing ethic of do no harm may not occur. According to McIntyre‚ Thomlinson‚ & McDonald‚ “nurses are held in high regard” (2006‚ p.360). As such‚ nurses must keep this positive concept‚ as we are the health professionals that care for people when they
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are two types of attribution‚ situational and dispositional. Dispositional factors are physiological such as mood‚ personality beliefs‚ situational factors are external factors that influence behaviour. The 2 main errors of attribution I will be discussing are fundamental attribution error (FAE) and self service bias (SSB). (FAE) is when people try to interpret someone else’s behaviour they often overestimate the role of dispositional factors‚ and often underestimate
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"A medication error is any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional‚ patient‚ or consumer” (NCCMERP‚ 2016). According to the Academy of Managed Care Pharmacy (2010)‚ medication errors are among the most common medical errors‚ harming at least 1.5 million people every year. Someone who has experience working with medications and the errors of them would be Laurie Dworkin. Laurie
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Data Warehousing‚ Data Marts and Data Mining Data Marts A data mart is a subset of an organizational data store‚ usually oriented to a specific purpose or major data subject‚ that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse‚ though in the bottom-up data warehouse design methodology the data
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The fundamental attribution error a more common name for the correspondence bias is defined as the tendency of people to make dispositional attributions for others behaviors. (Duff‚ 2012) For example‚ if a cashier failed to smile at you while checking out at the store‚ you might assume that they are just miserable and rude. You wouldn’t take anything else into consideration. You’d be judging their behavior based “who they are” rather than taking situational factors into consideration. The observations
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Despite the fact that this Court has found the Hurst error harmless in some cases without evidentiary development‚ this Court has never done so when a defendant has proffered evidence and been denied a hearing. Appellant requested a hearing based on a substantial evidentiary proffer showing the extent of the Hurst error in his case. This proffer cast doubt on what the outcome of his penalty phase would have been without the constitutional error. In light of Appellant’s proffer‚ the circuit court
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speaker‚ I often make written grammar errors comparable to Folse (2009)‚ “20 common errors made by native speakers” (p. 39). Therefore‚ when tasked with distinguishing native speakers and “ELL grammar errors” quiz (p. 4)‚ I completely missed the possessive “students’” in number 10. On the other hand‚ I was able to detect ELL grammar errors contributing this success to my frequent interaction with immigrant populations. The most apparent being word order errors. Receiving a low score in the “ELL grammar
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SMS CUSAT Reading Material on Data Mining Anas AP & Alex Titty John • What is Data? Data is a collection of facts and information or unprocessed information. Example: Student names‚ Addresses‚ Phone Numbers etc. • What is a Database? A structured set of data held in a computer which is accessible in various ways. Example: Electronic Address Book‚ Phone Book. • What is a Data Warehouse? The electronic storage of large amount of data by business. Concept originated in
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Chapter 1 Exercises 1. What is data mining? In your answer‚ address the following: Data mining refers to the process or method that extracts or \mines" interesting knowledge or patterns from large amounts of data. (a) Is it another hype? Data mining is not another hype. Instead‚ the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus‚ data mining can be viewed as the result of
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Abstract Ever since there has been crime there has been Crime Scene Investigations (CSI). Throughout the 1900’s and continuing into current day‚ technological advancements in CSI have been taken for granted. In the past‚ law enforcement (LE) agencies and investigators were not capable of having regular access to the tremendous amount of information that can be found and analyzed from a crime scene. Present days CSIs typical “tools of the trade” range from flat out boring every day devices to the
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