Psychosocial Characteristics of Breast Malignancy

Psychosocial Characteristics of Breast Malignancy Substantial resources have been shared out to research into the psychosocial characteristics of breast malignancy in the last twenty years. Initial studies in this field mainly focused on describing the emotional experience of women with breast malignancy and also attempted to develop interventions which can reduce psychosocial distress and prepare them to cop-up with the situation. Ferlic M, Goldman A, Kennedy BJ (1979) conducted a study titled â€Å"Group counseling in adult patients with advanced cancer† and reported a noteworthy enhancement in participants â€Å"perception and self-concept† and a similar benefit reported by Heinrich and Schag (1985). These two studies were referred as the early intervention studies among women with breast cancer. David Spiegel et al. (1989) found that women with metastatic breast malignancy can extend their survival by a psychological intervention (â€Å"supportive–expressive group therapy†). David Spiegel’s this report had various impacts on psychosocial intervention studies in1999s. After Spiegel’s surprising findings in 1989, the researchers shifted their focus from describing emotional experience of women with breast malignancy to survival outcomes of psychosocial interventions. Cunningham et al., (1998); Edelman et al., (1999a); Goodwin et al., (2001); and Classen et al., (2001) conducted different studies to find out the favorable outcome of psychological interventions on survival of women with metastatic breast malignancy. None of the succeeding studies in metastatic breast malignancy have recognized a survival effect of a series of psychological interventions. Several similar findings were reported among different cancer studies with the intention of surviv al outcome from their metastatic malignancy (Linn et al., 1982; Fawzy et al., 1993; Ilnyckyj et al., 1994; Kuchler et al., 1999). All these observations, from studies held in 1990s, forced the members of psycho-oncology research group to change their focus of assessment to the mental status and personal satisfaction of women with breast cancer, and to the recognition of interventions that positively influence their mental and social functioning, instead of metastatic breast cancer survival and their in-between biomedical outcomes. From 2000, a good number of psychosocial oncology researchers concentrated on focusing their research in the area of metal statues, wellbeing and quality of life of women during and after their active treatment for breast cancer. Antoni et al. (2001) explained â€Å"Cognitive-behavioral stress Management intervention decreases the prevalence of depression and enhances benefit finding among women under treatment for early-stage breast malignancy†. The writers observed the effects of ten-week group â€Å"cognitive- behavioral stress management intervention† in the midst of 100 women recently undergone treatment for stage 0-II breast malignancy and reported positive benefits after the intervention. Cruess et al. (2001) studied the impacts of a â€Å"cognitive-behavioral stress management (CBSM)† group intervention on â€Å"serum cortisol† stages in women being undergone treatment for breast cancer with stage I or II. Women who were in the Intervention group demonstrated improved benefit finding and decreased â€Å"serum cortisol† levels, whereas women who were in the control group not experienced any change. The statement by Kissane and colleagues (2003) of a randomized, controlled trial of cognitive–existential group therapy for women with early breast cancer is an example. In that research they found that women in the intervention group reported considerably lowered ‘anxiety†, and enhanced â€Å"family function†. The authors further reported self-growth and increased knowledge of cancer and its treatment. During this period numerous excellent reviews of psychosocial interventions in breast cancer have been published (Rimer et al., 1985; Fawzy et al., 1995; Meyer and Mark, 1995; Wallace, 1997; Burke and Kissane, 1998; Newell et al., 2002) and most of these reviews suggested that there are significant advantages associated with the use of psychological interventions during and after their active treatment. Further, these reviews suggested various intervention approaches such as: education, cognitive and behavioral training, individual psychotherapy, group interventions, and made more specific suggestions concerning incorporation of psychosocial interventions into the treatment setting. They emphasized that there was proof of benefit for all of these approaches, reporting that cancer patients may benefit from a variety of psychological intervention programmes, and recommending accurate interventions at different points along the cancer trajectory. Newell et al. (2002) conducted one review and achieved fairly different conclusions. The authors of this review attempted a broad survey of psychological treatments in various sorts of cancer. Further that they applied a sequence of thorough methodological standards and retained only those researches that achieved their standards of inclusions. This brought about the rejection of the greater part of published research. This review was comprehensive, but it did not focus on a specific type of cancer or a specific type of treatment, and the effects of interventions among different types of cancers did not differentiate by the reviewing team and that was considered as one of the major drawback of their review. Because of its strict inclusion criteria many important effects of psychosocial interventions being missed or undervalued. For the assessment of benefits they took an exceptionally progressive methodology in which at least half of the effective measures for the particular characte ristic need to account significant outcomes for the impact to be categorized as a significant one. The reviewing group observed the acute, intermediate, and durable effect of interventions on a huge number of results together with â€Å"anxiety, depression, hostility, stress or distress, general or overall affect, general or overall functional ability or quality of life, vocational or domestic adjustment, coping or coping skills, interpersonal or social relationships, sexual or marital relationships, pain, nausea, vomiting, fatigue, overall physical symptoms, conditioned nausea and vomiting, survival, and immune† effects. Newell et al., (2002) concluded that the support of the effectiveness of psychological management on distress and quality of life among people with cancer is uncertain. They also noticed a total lack of support for the effectiveness of the interventions for enhancing social functioning, even though this is a key feature of how patients outlook their revival and life after treatment (Schag et al., 1993; Carver et al., 2003). Antoni et al. (2004) reported that their outcomes are very much at odds with those conclusions. Then the question arises what is the cause of the disagreement? One major distinction between this study and those in Newell et al.’s (2002) review is the samples. Studies in that review scrutinized patients dealing with different cancers at different stages of illness and treatment, whereas Antoni groups sample was all women with breast cancer who were at the beginning of treatment. Other research on breast cancer has also revealed encouraging influences from such interventions (Andersen et al., 2004). For example, one trial of women with Stage II–III breast cancer explained that a group-based intervention that was paying attention on stress management, reduced anxiety, improved social support, enhanced diet, and reduced smoking (Andersen et al., 2004). That study, although valuable, exemplifies a major limitation in this field: a lack of evidence for the durability of the e ffects (Newell et al., 2002). Only one follow-up evaluation was accounted, which was right at the conclusion of the intervention. Here the new question arises whether the intervention effects last beyond the time of involvement, as patients go back to their home, their daily life, and their responsibility as partners, parents, and employees? Studies using more follow-ups are exceptional, even though outcomes of these interventions sometimes come out well after adjuvant treatments end (Andersen, 1992). The work of Antoni et al. (2006a) helps advance the field by reporting that a â€Å"CBSM intervention† can construct significant and long-lasting effects on measures representing an improvement of social functioning, decrease of negative effect, and enhances positive experiences. Certainly, it is remarkable that a number of the effects actually solidified from 6 months to 12 months. A comparable pattern also has been found in the trial utilized a different intervention that was put into practice at a different point in the active medical treatment (Scheier et al., 2006). It is significant to observe whether such consolidation is a consistent occurrence and how sturdy it is across time. Antoni et al., (2006a) strongly advocated that more studies track participants for longer times subsequent to the psychosocial intervention move towards to its conclusion.

Customer Satisfaction in E-Commerce

In Proceedings of the 17th IEE UK Teletraffic Symposium, Dublin, Ireland, May 16-18, 2001 QUANTIFYING CUSTOMER SATISFACTION WITH E-COMMERCE WEBSITES Hubert Graja and Jennifer McManis1 Abstract E-commerce is an increasingly significant part of the global economy. Users of E-commerce Web sites often have high expectations for the quality of service, and if those expectations are not met, the next site is only a click away. A number of performance problems have been observed for E-commerce Web sites, and much work has gone into characterising the performance of Web servers and Internet applications.However, the customers of E-commerce Web sites are less well studied. In this work, we discuss a way of assessing satisfaction for different customer types with a Web site according to various different parameters. Individual measures may be scaled for simple comparison, and combined to give an overall satisfaction rating. This methodology is applied to three Irish E-Commerce Web sites. 1) In troduction The World Wide Web is one of the most important Internet services, and has been largely responsible for the phenomenal growth of the Internet in recent years.An increasingly popular and important Web-based activity is ECommerce, in which various types of financial transactions are carried out or facilitated using the Web. It is widely expected that E-Commerce activity will continue to grow and that it will be a significant component of the global economy in the near future. A number of performance problems in E-Commerce systems have been observed, mainly due to heavier-thananticipated loads and the consequent inability to satisfy customer requirements. This has resulted in a lot of work attempting to characterise the performance of Web servers and Internet applications e. . [1]? [4]. However the customers of these E-Commerce systems are less well studied. Some surveys show considerable dissatisfaction with current E-Commerce and Web servers; for example, it has been repor ted that as many as 60% of users typically cannot find the information they are looking for in a Web site, even though the information is present [5]. In an area such as ECommerce, customers demand a high quality of the service they receive, since it is easy to move away to another site if they perceive the current one to be unsatisfactory. An important issue in designing E-Commerce systems is to characterise the ustomer's requirements for satisfactory service. Parameters which affect a customer's satisfaction with an E-Commerce system include the response time, number of clicks needed to find what they want, amount of information they are required to give, and predictability of the service received. This leads to the idea of customer classification, where customers in the same class would value parameters in a similar fashion. Customer classification may be performed either based on how they judge their satisfaction with an E-Commerce system, or on some other way (e. . large/medium /small budget; type/speed of Internet connection the customer has to the server; frequent/previous/new customer). Here we briefly present a methodology for measuring the satisfaction of customer classes. This methodology is applied to a test case consisting of three Irish E-Commerce Web sites in the telecommunications sector. We are able to demonstrate different levels of customer satisfaction among the Web sites, and also different levels of satisfaction with various parameters for each individual Web site. 2) MethodologyIn our methodology, we identify customer classes reflecting groups of customers with different behavioural characteristics, and Web site parameters relating to features of the Web site which will potentially affect customer satisfaction. We then seek to measure customer satisfaction with the various parameters in a consistent and quantifiable way. This methodology is summarised below; a more detailed discussion of the methodology may be found in [6]. 2. 1) Customer Classification Customers may be classified in various ways, such as their behaviour or according to how they measure satisfaction with a Web site.However this classification is made, a representation of the customer class must then be made. This representation has two components: first, customer behaviour; and second, customer satisfaction measures 1 Performance Engineering Laboratory http://www. eeng. dcu. ie/~pel School of Electronic Engineering, Dublin City University, Dublin 9, Ireland [email  protected] dcu. ie, [email  protected] dcu. ie for various Web site parameters. We define customer behaviour in terms of the interaction with the Web site. A trace behaviour is defined as the series of clicks and other information that the customer exchanges with the site.Typically, behaviour for a customer class is defined as one or more traces. For a customer class, a weighting may be associated with the traces indicating how likely it is for the customer to perform that particular trace behaviour. That is, some behaviour may be exhibited more frequently by a user in a class, and this behaviour should be given higher weighting. 2. 2) Customer Satisfaction Measures The factors which might affect customer satisfaction with a Web site are contained in a parameter list.It is important that for each parameter in the list satisfaction should be quantifiable. Some quantification measures are easily defined. For instance, if the parameter is the number of clicks, the quantification may be defined as an integer value. Other parameters may have more subjective quantifications. For instance, how does one quantify the â€Å"quality† of information available at a Web site? In order to compare the satisfaction measured for different parameters, the quantifications must be mapped to a fixed scale. For instance, all measures could be mapped to a scale of 0 to 10.This mapping is what allows us to represent customer valuation of the same parameters. For instance, some cu stomers will tolerate delay better than others. This may lead to one customer mapping a download time of 5 seconds to 10 and another mapping a download time of 5 seconds to 0. Studies such as [7] indicate that this mapping can be complex and context dependent. 2. 3) Analysis of Customer Satisfaction for a Web Site Using the above, for each trace it is possible to associate a satisfaction value with every parameter.The trace weightings may then be used to arrive at a weighted average of the satisfaction values associated with the parameters. This gives a measure of how satisfied a given class of customers is with a given parameter. Finally a weighting of parameters can be defined, allowing for an overall satisfaction measure of a class for the Web site. By varying this weighting, we can study how different parameters affect customer satisfaction. 3) Test Results The most difficult part of this exercise is in relating customer trace behaviour to the satisfaction vector. How parameter satisfaction is measured nd how it is mapped onto a fixed scale must be addressed on a case-by-case basis, although experience using the methodology may lead to the definition of some standard cases. Also, since multiple executions of the same trace may lead to different values, some statistical analysis may be required. We have applied our methodology to three Irish E-Commerce Web sites in the telecommunications sector (designated here as Web sites A, B, and C). 3. 1) Customer Classification Customers for the three Web sites we examined have been divided into two distinct classes: Private and Business.Traces are associated with searching for specific information that the customers might be interested in. Six customer tasks are identified in Table 1 and for each Web site a trace is devised to perform the task. For the sake of convenience, we call all traces associated with a given task by the same name, even though the trace is obviously specific to the Web site. Data services is sp lit into T4a and T4b because Web site B provided different pages depending on whether the customer was private or business.Trace T1 T2 T3 T4a T4b T5 T6 Task Where to buy a phone Coverage Tariffs WAP Data Services Data Services for Business Roaming List Business Tariffs Table 1: Tasks The Private and Business customer classes are defined as a collection of the above tasks, and an associated weighting is given which is indicative of the relative likelihood of customers of a given class seeking to perform that task. Trace weightings for the Private and Business classes are given in Table 2. The interpretation is that for a group of Private users roughly half might want to know where to buy a phone, 30% might want to know about tariffs, 10% ight want to know about coverage and 10% might want to know about WAP services. The Business users exhibit different behaviour with 30% wanting to know about coverage, 30% being interested in the roaming list, 20% being interested in data services an d 20% being interested in business tariffs. Customer Class Private Trace T1 T2 T3 T4a T2 T4a, T4b T5 T6 Trace Weighting 0. 5 0. 1 0. 3 0. 1 0. 3 0. 2 0. 3 0. 2 Business Table 2: Trace weightings for different customer classes 3. 2) Satisfaction Measures Three parameters were identified: Complexity, Time, and Quality.Complexity was measured as the number of clicks to reach the destination. Time was measures as total download time in seconds. Quality was a subjective measure of the quality of the information contained in the site (could the information be found, and how easy was it to find? ). Quality was measured using a small-scale user survey where the users were asked to examine the end page for each task and rate their satisfaction with the information they found there on a scale of 0-100%. A scale of 0-10 (with 0 being worst and 10 best) was chosen for a uniform comparison of satisfaction values.The measured satisfaction values were mapped onto the 0-10 scale as follows: Complex ity: Time: Quality: 10(20-(n-1)/10), where n is the number of clicks 10(10-t/60), where t is the trace download time in seconds x/10, where x is the average value of user satisfaction with the quality of the page For Quality a straightforward linear mapping was applied. More complex mappings were employed for Complexity and Time, and are shown in Figure 1. Examining the Time mapping we see that 60 seconds is regarded as an unacceptable download time, and even 30 seconds leads to a fairly poor rating.Similarly, for Complexity, 10 clicks is regarded as unacceptable, and even 5 clicks is fairly poor. Note that we have chosen one among many possible mappings. It is up to the tester to decide how to choose a mapping that best reflects customer preferences. Also note that, in this case, all customers use the same mappings, and thus are seen to perceive the parameters in a similar fashion. It is an easy extension to attach different scale mappings to different customer classes or to differ ent traces. Figure 1: mapping time and complexity measures to a 0-10 scale 3. ) Satisfaction Measurement for Web Sites Once the satisfaction measures are determined, it remains to test the Web sites and compare results. Data was gathered using the Web Performance Trainer 2. 1 tool [8] to execute each of the traces on the Web site in question. This was necessary solely to take time data, and was carried out on a weekday. The other two satisfaction values can be determined by an examination of the Web sites. Tables 3, 4, and 5 summarise the satisfaction measures for the three Web sites respectively. Web Site A Customer Class Trace Complexity rawSatisfaction Measures Time raw 37. 6 34. 0 34. 7 28. 6 34. 7 46. 9 28. 6 38. 7 scaled 2. 4 2. 7 2. 6 3. 3 2. 6 2. 6 1. 7 3. 3 2. 3 2. 4 Quality raw 80 72 67 68 61 69 66 64 scaled 8. 0 7. 2 6. 7 6. 8 7. 5 6. 1 6. 9 6. 6 6. 4 6. 5 scaled 4. 1 3. 0 4. 1 4. 1 3. 8 4. 1 3. 0 4. 1 4. 1 3. 8 Private Business T1 T3 T2 T4a weighted avg. T2 T5 T4a T6 wei ghted avg. 4 5 4 4 4 5 4 4 Table 3: Customer Satisfaction for Web Site A Web Site B Customer Class Trace Complexity raw scaled 4. 1 7. 4 5. 5 5. 5 5. 4 5. 5 4. 1 4. 1 7. 4 5. 2 Satisfaction Measures Time raw 16. 7 11. 2 17. 1 13. 9 17. 1 14. 39. 7 12. 3 scaled 5. 3 6. 5 5. 2 5. 9 5. 7 5. 2 5. 7 2. 2 6. 2 4. 9 Quality scaled 8. 6 7. 6 7. 6 7. 4 8. 1 7. 3 7. 5 6. 4 7. 6 7. 2 raw 86 76 76 74 73 75 64 76 Private Business T1 T3 T2 T4a weighted avg. T2 T5 T4b T6 weighted avg. 4 2 3 3 3 4 4 2 Table 4: Customer Satisfaction for Web Site B Web Site C Customer Class Trace Satisfaction Measures Complexity Time raw scaled 4. 1 5. 5 7. 4 5. 5 5. 0 7. 4 7. 4 5. 5 7. 4 7. 0 raw 14. 0 13. 0 11. 1 12. 4 11. 1 10. 2 12. 4 10. 9 scaled 5. 8 6. 1 6. 5 6. 2 6. 0 6. 5 6. 8 6. 2 6. 6 6. 5 Quality scaled 8. 1 6. 8 6. 8 5. 8 7. 4 6. 1 5. 3 6. 5. 3 5. 7 raw 81 68 68 58 61 53 60 53 Private Business T1 T3 T2 T4a weighted avg. T2 T5 T4a T6 weighted avg. 4 3 2 3 2 2 3 2 Table 5: Customer Satisfaction for Web Sit e C The overall satisfaction measures are summarised in Table 6. Some interesting conclusions can be drawn from these measures. Firstly, for all Web sites and all parameters, there was a variation in satisfaction levels between the customer classes. Thus, not all users find the Web sites equally good. This is most noticeable for the Quality parameter: Private users rated Quality higher than Business users in all cases.If Business customers are considered valuable, this gap is not desirable. There is also a large difference in satisfaction ratings for the Time parameter of Web site B, again favouring Private customers over Business customers. Secondly, for all users and all measures, there are a range of values across the Web sites. For instance, the Time satisfaction for Business users varies from 6. 5 for Web site C down to 2. 4 for Web site A. This indicates that Web site C might have an edge in attracting Business customers. Finally, for a given user class and Web site, different satisfaction levels are observed.For example, Private users of Web site A have a Time satisfaction value of 2. 6 and a Quality satisfaction value of 7. 5. The exact interpretation of this is difficult, since the different parameter satisfaction values are dependent on the mapping of the raw data, which of necessity, differs for each parameter. However, it does perhaps indicate a favouring of form over efficiency. Customer Class Satisfaction Customer Web Site Class Web site A Private Web site B Web site C Web site A Business Web site B Web site C Satisfaction Measures Complexity Time Quality 3. 8 5. 4 5. 0 3. 8 5. 2 7. 0 2. 6 5. 7 6. 2. 4 4. 9 6. 5 7. 5 8. 1 7. 4 6. 5 7. 2 5. 7 Table 6: Customer Class Satisfaction for Web sites A, B, and C Finally, an overall assessment of customer satisfaction may be found by weighting the various parameters. Table 7 displays the overall satisfaction results under several different weighting schemes: Weighting 1 gives all parameters equal weighting ; Weighting 2 gives Time and Complexity equal weighting and Quality zero weighting; Weighting 3 considers Time only (zero weighting for Quality and Complexity). These weightings reflect possible values the tester places on the various parameters.We can see that for all the weightings, Business users have a clear order of preference, ranking Web site C highest, then Web site B, and finally Web site A. The order of preference for Private users varies according to the weighting used, although Web site A is worst under all three weightings. Customer Class Satisfaction Customer Web Site Class Web site A Private Web site B Web site C Web site A Business Web site B Web site C Satisfaction Measures Weighting 1 Weighting 2 Weighting 3 4. 6 6. 4 6. 1 4. 2 5. 8 6. 4 3. 2 5. 6 5. 5 3. 1 5. 1 6. 8 2. 6 5. 7 6. 0 2. 4 4. 9 6. 5 Table 7: Customer satisfaction with a Web site ) Conclusions Modelling customer satisfaction with Web and E-commerce sites is not as well studied as Web server modelling, but determining whether and how the customers of these sites are satisfied with their interactions is becoming increasingly important as the Web matures. We have proposed a methodology for estimating how satisfied defined classes of customers are with a Web site. Our approach recognises that customer satisfaction is a complex issue and includes factors which are not easily measured. We have applied our methodology to the study of three Irish E-Commerce Web sites.These sites were chosen for representative purposes only and the results do not necessarily generalise to other Web sites. Choices for the tester include not only what customer categories and what Web site parameters to examine, but also how to interpret the measured data such as download time. The flexibility of the methodology means that it will be necessary for the tester to carefully consider all of their options. The next step is to investigate whether ‘generic’ categories of users can be defined, and/or wh ether they care about ‘generic’ Web site parameters (e. . it seems download time will always be a factor in user satisfaction). Given a specific Web site, we will explore methods for mapping these generic user types and satisfaction parameters into the site's content. If an analysis of the resulting satisfaction measures shows that there is a disparity in the satisfaction of different user types, we will study how the Web site designer or administrator should take this into account, and whether their reaction can be determined dynamically while the user is interacting with the site.References 1. 2. 3. 4. 5. 6. 7. 8. Nakamura et al, `ENMA: the WWW Server Performance Measurement System via Packet Monitoring', INET99. Cottrell et al, `Tutorial on Internet Monitoring and PingER at SLAC' available from http://www. slac. stanford. edu/comp/net/wan-mon/tutorial. html Kalidindi and Zekauskas, `Surveyor: An Infrastructure for Internet Performance Measurements', INET99. Hava and Murphy, `Performance Measurement of World Wide Web Servers' Proc. f 16th UK Teletraffic Symposium, May 2000. http://www. ecai. ie/usability_online. htm Graja and McManis, ‘Modelling User Interactions with E-Commerce Services’, to be presented at ICN01, Colmar, France, July 2001. Bouch, Kuchinsky, and Bhatti, ‘Quality is in the Eye of the Beholder: Meeting Users’ Requirements for Internet Quality of Service’, HP technical report HPL-2000-4, http://www. hpl. hp. com/techreports/2000/HPL-2000-4. html Web Performance Incorporated, http://www. Webperfcenter. com

Project List

The second type of users Is a technical, who Is allowed to update the status of a certain computer part (fixed, not fixed, etc. ). The third type of users Is the system Administrator, who has the ability to add and remove technician and user accounts. 2. A Car Rental System This system will allow for three types of users: guests, members, and administrators. Guests will be able to browse location, availability, price, and model. Members will have their personal information stored (I. E. Name, address, and credit card info. ) and will have access to any specials.Finally, the administrator can change or update car models, prices, etc. 3. A Flight Reservation System Users will be able to look for, book and cancel flights, as well as, organizing trips. There are 3 different types of users. The administrator will be able to add/delete destinations, change prices and so on. The registered users will be able to book/ cancel flights. Finally, the guests will be able to search for flights, bu t they won't be able to reserve them unless they register _ 4. A Grade Report System This system allows a professor logs on to create, access, and updates class grades for dents in his or her class.The students in the class are then able to log on and check their scores for all exams taken in that class. A system administrator is responsible for logging in and adding/deleting students, teachers, and courses from the mall database. 5. A Movie Store System This Is a system for selling and buying DVD's and videos of movies. There will be three types of users. First the regular customers, they can access the database of DVD's and videos with different types of search. Second there is a group of users that can post DVD's or/and videos to be sold.These users have access to add movies to the database, so that regular customers can search for these movies. When the customer has finished searching for a DVD and/or a video he/she can communicate a message to the seller In order to buy the pro duct from him. The third class of user Is the administrator: this user will be in charge of administrating the database and users. The administrator will be in charge of giving and revoking selling privileges to regular customers so that they are able to add videos and DVD's in the database. . A Health Insurance System This system allows agents and customers to view and update Important Information. Heir agent's information, family members, etc. Agents will be able to view and update their personal information and view the information about new businesses, renewals and commissions. This way, they will be able to help any policyholder or agent requesting assistance. Managers would have access to all modules so they will be able to help any policyholder or agent requesting assistance and would be able to update database with new prices. In addition, guest will be able to get free quotes. 7. A Medical Clinic Tracking System This system will provide the doctors and their staff with an e lectronic copy of the patient file.The system will track information for billing purposes and for general management of the clinic such as reporting and document creation. The three levels of access are as follows: (1) Administrator: creates users, assigns roles, and maintain certain questionnaires in the application; (2) Support Staff including nurses, secretaries, and nurses aid: update, delete, and insert records, updating the system o reflect the actual hard copy of the patients file, also will run certain reports and letters generated by the system; and (3) Doctors: will have the same rights as the support staff plus the ability to access certain information via the web. . A University Registration System The University registration system will allow (1) registered students to view their current term schedules, (2) registrars to process students' requests for adding and dropping classes. The system administrator will be able to add and delete students and also will be able to o pen new classes. The system administrator will be also able to determine the number of students in any given class. 9.A Library System This system will allow the users to search for library material (book, magazines, videos, etc. ) according to the criteria specified. This system will also keep track of all the material circulation and their availability. There are three types of users. A patron can borrow and return library material. A librarian can update library material. The system administrator can manage the user accounts including both patrons and librarians.

How Genetics And Environment Affect A Child s Behaviors

Many researchers argue whether genetics or the environment, play a greater factor in the future behaviors of children. Some articles did experiments which indicated nature is a strong possibility in children’s behaviors; while the opposing articles experiments showed that nurture is also a strong determinant. The goal for this paper is to show how genetics and environment both affect a child’s behaviors. The first professional article that was read is called â€Å"The Role of Nature and Nurture for Individual Differences in Primary Emotional Systems: Evidence from a Twin Study† by Christian Montag, Elisabeth Hahn, Frank M. Spinath, Martin Reuter, Ken Davis, and Jaak Panksepp. The conductors of the experiment believe that primary emotional systems are important to finding out more about the psychological reasons for disorders in human beings. So the experiment they decided to do was on how important genetics and environment play a major role in the differences between twins. The twin’s emotions were measured using the Affective Neuroscience Personality Scales (ANPS) some of the participants involved were twins already in past experiments. These participants were found from Professor Dr. Spinath’s online experiment (Twin Game) it is a study of twin’s internet consumption and how it changes their personalities. The participants for the online game have to be at least 18 years of age. The twins fill out a questionnaire independently and it takes about an hour or so to finish. TheShow MoreRelatedIs Problematic Behavior Genetics Or Learned?952 Words   |  4 PagesProblematic Behavior Genetics or Learned? : A Review of Literature Psychologists have been studying problematic behaviors for years. In some cases problematic behaviors can come from the same household and act totally different from one another. 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Or, is there a relationship between nature and nurture with regards to child development? The debate continues. While some believe â€Å"nature and nurture work together, others believe they are separate and opposing influences† (McDevitt, 2010). Nature vs Nurture In regards to the nature vs. nurture debate, â€Å"this debate is a major issue in many social science disciplines and isRead MoreNature vs. Nurture1218 Words   |  5 Pagesresponsible for these differences? Is it simply that they are two different people with different interests and preferences? Or did the environments that they grew up in play a part in making who they are? In the nature vs. nurture controversy, nature proclaims that our genetic make-up plays the primary role in human development, while nurture declares that our environment dictates our development. The nature vs. nurture controversy is an age old question in the scientific and psychological world withRead MoreChild Development: Heredity and Environment1240 Words   |  5 Pagesor even environment versus heredity leads to the question of: does the direct environment or the nature surrounding an adolescent directly influence acts of delinquency, later progressing further into more radical crimes such as murder or psychotic manifestation, or is it directly linked to the hereditary traits and genes passed down from that individual adolescent’s biological parents? To answer this question one must first understand the difference between nature, nurture, environment, and heredityRead MoreChildhood Obesity Is A Growing Epidemic1297 Words   |  6 PagesChildhood obesity is a growing epidemic. The UK has estimated through their schools’ National Child Measurement Program that one-third of the children there are overweight, and by 2050 that number could rise to an alarmi ng two-thirds (Phillips 2). There are many uncontrollable factors in childhood obesity such as the environment, income and genetics. However, parents are the most overlooked factor. Our children’s futures, with regard to their eating habits, are in the hands of their parents. UltimatelyRead MoreThe Effects Of Genetic Traits And Society Impact On Child Development1479 Words   |  6 Pagesmany factors affecting child development, and currently there is an ongoing debate comparing the effect of genetic traits and societies impact . Even though hereditary traits affect development, society has a greater effect on child development. This is because learning environment, socialization, and interaction with family and friends can impact a child in a number of ways that affect how they develop.Children s medical services describes child development as : Child development is typically