Patient Education Material
Patient Education Material
[Name of the Writer]
[Name of the Institution]
Patient Education Material
Here we have an example of the PEM distributed among the patients within the hospitals, developed by the UPMC health library. UPMC is a famous health care providing company and insurance agency focused on their patients care. UPMC has more than 30 hospitals, with more than 600 doctors. I have chosen a patient education material from the website of UPMC, which was all about the traveling of cancer patients, and their precautions. According to Wilson (2009), more than 90 million people in the USA have less knowledge about primary health care, which leads them to poor health conditions. To assess the readability of PEM is a vital function of health education.
We will study the literacy level of this patient education material, by using the FOG, SMOG, and FRY Readability.
Fog Index is also called the Gunning Fog index, and it is used to measure the reliability of an English writer. It estimates the level of literacy required to understand a passage at the first reading. To calculate fog, it is necessary to select a document with less than 30 sentences. Then one has to count the total number of words in the given passage. Exclude all the numbers, acronyms or websites links, etc. Count three or more syllables words in the chosen document.
Furthermore, count the number of sentences in the given documents. Find the average words used in the sentence. Find the percentage of big words, then add the averge length of a sentence with the percentage of big words. The result gotten from the addition of these two number will be multiplied with 0.4. Finally, the product we got is the Fog Index.
This passage includes 450 words, and big words are 46 in total. The total number of sentences are 30. Percentage of big words is 10.2 percent, while the average words in a sentence are equal to 15. The sum of both values is 25.2. Now multiply it with 0.4 which will be 10.08.
The Fog Index = 10.08
Now we will calculate SMOG for the given passage. Select a document of 30 sentences, divide into three parts of 10 sentences each. Find three or more syllables words in each group. Take under the root of the number of words with more than three syllables. Then add three with the figure the SMOG will be calculated this way.
SMOG= 3+ Square root of polly syllable word count
12 +17+ 16= 45 pollysyllable words
SMOG =3+ 6.7
SMOG = 9.7
The Fry Readability or also known as Flesch, it gives the more accurate result to measure the readability of a 100 words sample document.
Its formula is to divide the number of words (i.e., 100 words) by the number of sentences, which are 7. The result is 14.28.
Now multiply the value with 1.015 to get the value of x.
X = 14.5
Next, count the number of polysyllables, and divide by the total number of words. Then multiply the value with 0.846, this value will be recorded as Y.
14/0.86 = 16.28
Y = 16.28
X + Y = 30.78
It shows that the passage is Difficult College level.
The readability score provides information regarding the level of understanding of the readers of a document or a passage. The Fry Readability provide information regarding the grade level of the reader. It doesn’t need real readers to investigate the level of reading. Nevertheless, it is purely a quantitative approach. For instance, it provides information about the level of text and suggests that x- grade student or qualified person can understand it. Though the level of understanding varies from person to person. Fry graph is effective for the basic and in-depth information sharing with the targeted clients (Betschart et al., 2017).
Fog is used to know either the targeted readers understand the given piece of reading. The acceptable value of Gunning fog is less than 12, it is mostly used for generalized readers when the range of audience is broader and need some general reading.
SMOG stands for a simple measure of Gobbledygook. It is extensively used for the readings of health care issues. It is important to know about the literacy and understanding of the people before the distribution of a piece of reading. It is mostly used by the health departments, to spread a message for awareness of the general public. So the passage should be analyzed by these techniques before offering to the general public. The writer should also consider the expected audience and their level of education and understanding. The writer should avoid the use of technical terms, or try to minimize the use of specialized medical terms extremely. The health care information is available online to reach to maximum audience. Internet helps the patient to know more about the disease and the preventive actions (Prabhu et al., 2016).
The health care and treatment system has been changed during the last few decades. Due to the emergence of new diseases, and the latest changes in medical sciences. The focus is to educate and aware the patients regarding health issues, prevention, and precautions. It has been noticed in every hospital, clinics, and other health-related centers, the display of public awareness messages. These messages are in very easy languages, with least or no technical terms. The main objective of these messages is to convey an important message to maximum people. Regardless of the level of education, and the field of study, everyone can get benefits from these messages. There are several techniques which help the writer to know about the level of difficulty of the message.
Betschart, P., Zumstein, V., Bentivoglio, M., Engeler, D., Schmid, H. P., & Abt, D. (2017). Readability assessment of online patient education materials provided by the European Association of Urology. International urology and nephrology, 49(12), 2111-2117.
Prabhu, A. V., Hansberry, D. R., Agarwal, N., Clump, D. A., & Heron, D. E. (2016). Radiation oncology and online patient education materials: deviating from NIH and AMA recommendations. International Journal of Radiation Oncology* Biology* Physics, 96(3), 521-528.
Wilson, M. (2009). Readability and patient education materials used for low-income populations. Clinical Nurse Specialist, 23(1), 33-40.
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