Sampling, Data Collection, Validity and Applicability

The article addresses the issue of malnutrition which is defined as the imbalance or under-nutrition that is normally seen in the developing world and at times in some parts of the developed world. It is believed that malnutrition is caused by a deficiency in the dietary intake by the patients. In some cases, malnutrition can occur as a result of increased requirements that may be associated with other disease states and conditions (Crowe et al. 2009). For instance, if a patient suffers from a disease that makes them go through a poor absorption of food substances, they may most likely suffer from malnutrition. Moreover, if a patient goes through an excessive loss of nutrients, then chances are so high that they may end up suffering from malnutrition.

According to the article, the author does not give any age gap that may most likely suffer from malnutrition. The author suggests that a rough figure of 35% to 55% of the patients that enter an acute hospital have a high chance of suffering from malnutrition. Additionally, the demographics show that the prevalence of patients who suffer from malnutrition in a majority of hospitals ranged between 20% to 50%. That figure is however, in most cases dependent on the population definition as well as the criteria that are used for the diagnosis. For instance, there was a study that was conducted in Australia in the year 2009 where 275 patients were randomly assessed for malnutrition problems. The study was able to establish that 23% of the patients were actually suffering from malnutrition (Katan et al. 1997).

The data collection process was collected through the use of a malnutrition screening tool that was simply composed of a three questioned sample tool. The sample tool was used to assess whether the patients had a recent loss of either their eating appetite or even a loss in their weight (Gemert et al.  2008). The details that were collected from the patients were generally referred by medical surgical doctors because it helps in getting accurate history information regarding their patients. The malnutrition screening tool was specifically designed by for use by the non-nutrition trained staff. Such staff members could use the tool to easily identify the patients that were at a higher risk of getting malnutrition. It is through the knowledge that the nurses and nutrition experts can then provide the most appropriate diets to the patients based on the information collected. Moreover, the tool was also designed in such a way that it was able to detect malnutrition not only in children but also in adults. Additionally, the tool was able to detect if an adult suffered from obesity problems. Apart from that, scores were also derived from the measure of the body mass index of the patients as well as the chances of experiencing an unexpected weight loss. Moreover, the scores were also derived based on either the presence or the absence of a serious disease in the body of the patient. The design of the tool is designed in such a way that it could give reliable results apart from the case of renal patients.

In summary, the author believes that the instrument was the best to use in this case because it gave the nurses the required results. However, his only problem was that it had been validated to be used in young children as well as the renal patients. However, with a little improvement and innovations, the tool could emerge to be the best source of getting reliable information.

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