Readability, Reliability, and Quality of Nursing Care Plan Texts Generated by Chatgpt

Readability, Reliability, and Quality of Nursing Care Plan Texts Generated by Chatgpt | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Readability, Reliability, and Quality of Nursing Care Plan Texts Generated by Chatgpt Mucahide GOKCEN GOKALP, Sebnem CINAR YUCEL, Zehra CAKIR, EMRAH BOYUN, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7237457/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in BMC Nursing → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Research on ChatGPT-supported nursing care plan texts plays a critical role in making nursing education more innovative and accessible. These studies strengthen education by improving the readability, reliability, and quality of the texts. Purpose: This study aims to evaluate the readability, reliability, and quality of nursing care plan texts generated by ChatGPT. Methods: The study sample consisted of 50 texts generated by ChatGPT based on selected nursing diagnoses from NANDA 2021–2023. These texts were evaluated using a descriptive criteria form, the DISCERN tool, and readability indices including the Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index, and Flesch-Kincaid Grade Level (FKGL). Results: According to our findings, the readability level of the nursing care plans generated by ChatGPT was significantly higher than the recommended 6th-grade level (P < .001). The mean DISCERN score was 45.93 ± 4.72, indicating a moderate level of reliability for all evaluated texts. Additionally, 97.5% of the texts also achieved moderate scores on the information quality subscale. A positive and statistically significant correlation was found between the number of verifiable references and both the reliability (r = 0.408) and quality (r = 0.379) scores of the texts (P < .05). Conclusion: It should be noted that these AI-based chatbot tools cannot replace comprehensive patient care plans. In AI applications, it is recommended that the readability of generated content be improved, reliable references be included, and all outputs be reviewed by a professional team. artificial intelligence chatbot ChatGPT nursing care nursing education nursing care plan 1. Introduction ChatGPT is an artificial intelligence-based chatbot designed to generate human-like text and understand natural language commands. Developed by OpenAI, this model is one of the largest language models, with 175 billion parameters, and has marked a significant breakthrough in the field of natural language processing ( 1 ). It is transforming human-machine interactions across various domains, including education, healthcare, and customer service. ChatGPT is a multilingual language model trained on a diverse 570 GB text dataset (including books, articles, websites, etc.) ( 2 ). The model has been enhanced through reinforcement learning from human feedback (RLHF), enabling it to better understand user intent and generate coherent, fluent, and contextually appropriate responses aligned with human expectations ( 3 ). Despite ChatGPT's advanced language capabilities, it has significant limitations. In a study conducted by Gao et al. (2023), it was found that scientific article abstracts generated by the model were largely identifiable by AI detection tools, and 14% of these abstracts contained fabricated information ( 4 ). Furthermore, when faced with insufficient data, the model tends to generate inaccurate information and cite non-existent sources ( 5 ). Therefore, the readability, reliability, and accuracy of texts generated by ChatGPT must be carefully evaluated, especially in academic contexts. Borji (2023) points out that ChatGPT can produce inconsistent, biased, or contextually irrelevant responses and frequently suffers from the problem of "hallucination" (fabricated content) ( 6 ). This raises concerns about the model’s reliability and emphasizes the importance of using it cautiously and under supervision as a supportive tool. Among the limitations of ChatGPT are its inability to process data beyond June 2024, its tendency to repeat citations, and its particular unreliability in compiling medical information accurately ( 2 ). These factors restrict the model’s usability in fields such as healthcare, which require continuously updated information. According to an evaluation by Gordijn and Have (2023), although ChatGPT’s texts are linguistically fluent, they often contain repetitive content and lack academic depth ( 7 ). Therefore, it has been stated that ChatGPT is not reliable in terms of scientific integrity and cannot replace human researchers ( 7 ). Additionally, Borji (2023) noted that the model falls short in meeting essential academic writing requirements such as originality, critical analysis, and source verification ( 6 ). These findings indicate that ChatGPT should only be considered a supportive tool in academic writing and must be used under human supervision. The release of ChatGPT in 2022 sparked debates about its impact on writing assignments in academic education. There are concerns that students might use this tool unethically for completing assignments and projects ( 8 ). The existing literature shows that ChatGPT has become a popular tool among undergraduate and graduate students for producing academic texts and assignments ( 9 ). This situation has led to new discussions about how artificial intelligence tools should be evaluated in the context of academic integrity. Stokel-Walker (2023) stated that ChatGPT is capable of writing referenced articles and providing consistent answers to exam-type questions, which increases the risk of plagiarism among students. Academics are concerned about the impact of such tools on academic assessments ( 10 ). A study by Smith and Lee (2023) analyzing 10,732 tweets confirmed concerns about the potential misuse of ChatGPT and its negative effects on education ( 11 ). Similarly, a study conducted by Huh (2023) in South Korea found that although ChatGPT performed worse than medical students, it provided acceptable answers on a parasitology exam ( 12 ). These findings highlight both the potential and limitations of artificial intelligence models in education. ChatGPT provides significant contributions to nursing educators in preparing course content, educational materials, and case scenarios ( 13 ). It has been noted that AI-supported visuals enhance visual learning and reflection across different nursing education settings ( 13 ). In a study by Chang et al. (2023), nursing students who received physical examination training through an AI-based chatbot demonstrated improvements in academic achievement, critical thinking skills, and motivation ( 14 ). Additionally, ChatGPT’s ability to quickly compile patient information, generate comprehensive care plans, and detail nursing interventions contributes to time savings and increased efficiency in care planning ( 13 , 14 ). At this point, the readability level of AI-supported nursing care plans is a key factor determining the effectiveness of information transfer among various stakeholders, including clinicians (doctors, nurses), learners (nursing students), and service recipients (patients, caregivers). Even experienced professionals working in busy clinical practice environments require concise and clear language to process information quickly and accurately, reinforcing the scientific and practical importance of this issue. These capabilities make artificial intelligence a valuable tool in nursing education and care processes and encourage its use. Purpose and Research Questions The primary aim of this study is to evaluate the readability, reliability, quality, and originality of nursing care plans generated by artificial intelligence. In nursing education, students are assigned care plan tasks in various clinical fields; these plans form the foundation of nursing practice ( 15 , 16 ). Within this context, the study seeks to answer the following questions: Regarding nursing care plan texts; Is their readability at the recommended 6th-grade reading level? What are the quality scores? How are the reliability subscale values evaluated? How is the level of originality in terms of similarity rates? Do the text length (word count), the number of cited sources, and originality (similarity rate) affect how quality and reliable the nursing care plan texts written by ChatGPT are? 2. Materials and Methods 2.1. Research Design and Sample This study aimed to evaluate texts generated by ChatGPT. The sample of the study was determined based on the Central Limit Theorem. This theorem states that, with a sufficiently large sample size, the average of many random variables tends to approximate a normal distribution, allowing sample statistics to reliably estimate population parameters ( 17 ). According to the Central Limit Theorem, sample sizes larger than 30 increase the power to represent the population ( 18 ). In this study, to further enhance the generalizability and representativeness of the results, a sample size of 50 was chosen instead of the minimum recommended 30. Accordingly, 50 different nursing-related texts generated by ChatGPT were analyzed. 2.2. Selection of Topics In this study, the topics for the texts to be generated by ChatGPT were selected from the 2021–2023 taxonomy of the North American Nursing Diagnosis Association (NANDA) International, which includes 267 different nursing diagnoses ( 19 ). After numbering the 267 nursing diagnoses, the researchers created a randomization table using the website http://www.random.org (see Supplementary Digital Content, Randomization table.docx). Through this table, 50 nursing diagnoses were selected using a simple random sampling method. This method ensures that each diagnosis has an equal chance of being selected, guaranteeing the impartiality of the selection process ( 20 ). 2.3. Data Collection This study followed a rigorous data collection process to evaluate ChatGPT’s ability to generate texts that meet academic standards. Data were collected on June 20, 2025, using the ChatGPT web interface (June 20 version) available at ai.com. For transparency and reproducibility, access was made via Google Chrome (version 109.0.5414.119) running on Windows; these details are important for verifying the findings ( 21 ). Within the scope of the study, for each randomly selected nursing diagnosis, ChatGPT was prompted with the instruction: “Please write a text related to -NANDA DIAGNOSIS- and a nursing care plan. Use the maximum number of references and include citations within the text.” The purpose of this prompt was not only to generate information but also to assess ChatGPT’s ability to integrate academic references and provide citations. The phrase “nursing care plan” was specifically emphasized to ensure that the content was structured and clinically oriented. The capacity of AI models to generate texts with academic references is a significant topic in current research ( 22 ). All texts generated by ChatGPT were securely saved by the researchers for detailed evaluation and analysis in subsequent stages. 2.4. Data Collection Tools 2.4.1. Descriptive Information Assessment The descriptive information form consisted of 12 questions covering word and paragraph count, similarity rate (using iThenticate and Turnitin software; iThenticate, 2024; Turnitin, 2024), number of references, publication year, type, and accessibility of the texts ( 23 , 24 ). 2.4.2. Readability Assessment The readability of nursing care plan texts generated by ChatGPT was analyzed using two different online tools with evaluation capabilities. These tools were ReadabilityFormulas.com (Calculator 1) and Online-Utility.org (Calculator 2), each employing different readability formulas to assess the texts ( 25 ). The analysis utilized seven different readability formulas: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFOG), Simple Measure of Gobbledygook (SMOG), Automated Readability Index (ARI), Coleman-Liau Index (CLI), and Linsear Write (LW). Using these formulas, the readability score of each text was calculated, and the results were reported as median (minimum–maximum) values. The accepted readability level was set at 80 or above for the FRES formula, and 6 or above for the other six formulas ( 26 , 27 ). The readability, quality, and reliability of the texts were independently evaluated by two nursing experts (M.G. and Z.C.). Final scores were determined by averaging the results of both researchers. Details and calculation logic of the seven different readability formulas used in the evaluation are presented in Table 2 . 2.4.3. Reliability and Quality Assessment DISCERN, developed through a collaboration between the British Library and the National Health Service (NHS), is a tool designed to measure the reliability of online health information ( 28 ). This tool consists of three sections and 16 questions: the first eight questions assess reliability, the next seven evaluate the quality of information regarding treatment options, and the final question measures overall quality. Questions are scored from 1 to 5, resulting in a total score ranging between 16 and 80. According to the scoring, scores below 40% are considered poor, between 40% and 79% moderate, and 80% or above good quality ( 29 ). 2.5. Statistical Analysis All analyses were conducted using SPSS version 25 (IBM Corp., Armonk, New York). Descriptive analyses included frequencies, percentages, means, standard deviations, and ranges. The relationship between numerical variables and DISCERN scores was tested using Spearman correlation analysis. Turnitin and iThenticate programs were used to determine the similarity rates of the texts. In cases requiring comparison of continuous data, the Mann-Whitney U and Wilcoxon tests were applied. To assess the consistency of the calculators, intraclass correlation coefficient (ICC) analysis was performed for each formula. The reliability of the evaluation between two independent observers was determined by calculating Cohen’s kappa (κ) value. A p-value less than .05 was considered statistically significant. 3. Results The texts generated by ChatGPT based on randomly selected nursing diagnoses were thoroughly examined by two different researchers. A high level of agreement between the evaluators was found (Cohen’s Kappa coefficient κ: 0.827), indicating a high reliability of the texts. The average word count of the texts was 366.95 ± 57.98, ranging from 402 to 698 words, showing that the texts were generally short. It was determined that most of the sources used in the texts were fictional and could not be found in academic search engines such as PubMed or Google. Nevertheless, similarity checks conducted with platforms like iThenticate and Turnitin revealed very low plagiarism rates. An important finding was that, according to analyses performed using Turnitin’s AI detection technology, 100% of all examined texts were confirmed to have been written by artificial intelligence (Table 1 ). Table 1 Descriptive Information Form Variables Min-Max Mean ± SD Publication years of references 1984–2024 2018 ± 6,05 Number of references 4–7 5,6 ± 1,07 Number of accessible references 0–4 0.73 ± 1,01 Number of web references 1–7 2.14 ± 1.88 Number of accessible web references 1–4 0.25 ± 0.58 Number of article references 1–6 2.25 ± 1.25 Number of accessible article references 1–3 0.23 ± 0.58 Number of book references 1–2 1.66 ± 0.52 Number of accessible book references 1–2 0.25 ± 0.59 Word count 402–698 538 ± 110.26 Paragraph count 4–8 5.65 ± 1.00 iThenticate similarity rate 0–10 5.03 ± 3.75 Turnitin similarity rate 0–20 9.25 ± 3.75 Turnitin AI rate 100 100 ± 0.00 3.1. Readability Assessment of ChatGPT Using Average Scores from Calculator 1 and 2 When the average readability scores of the texts generated by ChatGPT were compared to the sixth-grade reading level standard, statistically significant differences were found across all measures (P < .001). This clearly indicates that the readability of the texts is above the sixth-grade level. Additionally, statistically significant results were obtained when comparing the results of Calculator 1 and Calculator 2 as well as their averages (P < .001) (Tables 3 – 4 ). 3.2. ChatGPT-4.0 Analysis Outcomes Utilizing Average Results from Calculator 1 and 2 The readability levels of nursing care plan texts generated by ChatGPT-4.0 for specific nursing diagnoses were examined. The analysis revealed a median Gunning Fog (GFOG) score of 17.34 (range: 13.38–24.28) and a median Flesch Reading Ease Score (FRES) of 30.69 (range: 9.30–53.25). Furthermore, the median SMOG and Flesch-Kincaid Grade Level (FKGL) scores were 14.20 (range: 11.24–18.97) and 14.27 (range: 11.20–20.58), respectively, indicating an approximate education level of 14 years. Similarly, the Automated Readability Index (ARI) score was 15.78 (range: 12.32–22.22), and the Coleman-Liau Index (CLI) score was 14.72 (range: 10.63–22.21), corresponding to a comparable education level (Table 4 ). Table 2 Readability tools, formulas and descriptions Readability index Description Formula Flesch Reading Ease Score(FRES) It was created to assess the readability of newspapers and is particularly effective for evaluating school textbooks and technical manuals. This standardized test is utilized by numerous US government agencies. The scores range from 0 to 100, with higher scores indicating greater ease of reading. I = (206.835 − (84.6 X (B/W)) − (1.015 X (W/S))) Gunning FOG (GFOG) It was designed to assist American businesses in enhancing the readability of their written content and is applicable across various disciplines. It estimates the number of years of education required for a person to understand a given text. G = 0.4 X (W/S+((C*/W) X 100)) Simple Measure of Gobbledygook (SMOG) It is typically appropriate for middle-aged readers, ranging from 4th grade to college level. While it aims to test 100% compre-hension, most formulas measure about 50%-75% comprehension. It is most accurate when applied to documents that are at least 30 sentences long. It measures the number of years of education the average person needs to understand a text. G = 1.0430X √ C + 3.1291 Flesch–Kincaid grade level (FKGL) Part of the Kincaid Navy Personnel test collection, it was designed for technical documentation and is suitable for a wide range of disciplines. Delineates the academic capacity level imperative for grasping the written material G = (11.8 X (B/W)) + (0.39 X (W/S)) − 15.59 Automated readability index (ARI) The ARI (Automated Readability Index) has been utilized by the military for writing technical manuals. Its calculation provides the grade level required to comprehend the text. Assesses the scholastic rank in American educational institutions needed to be capable of comprehending written material. The greater the number of characters, the more complex the term. ARI = 4.71 X l + 0.5*ASL − 21.43 Coleman–Liau (CL) score It is designed for middle-aged readers, spanning from 4th grade to college level. The formula is based on text with a grade level range of 0.4 to 16.3 and is applicable to many industries. Evaluates the educational level required for understanding a text and offers an associated grade level in the US education system. G = (− 27.4004 X (E/100)) + 23.06395 Linsear Write (LW) It was developed for the United States Air Force to assist in calculating the readability of their technical manuals. Offers an approximate assessment of the academic level needed to comprehend the text. LW = ( R + 3C)/S Result If > 20, divide by 2 If ≤ 20, subtract 2, and then divide by 2 ASL = the average number of sentences per 100 words, B = number of syllables, C = complex words (≥ 3 syllables), C* = complex words with exceptions including, proper nouns, words made 3 syllables by addition of “ed” or “es,” compound words made of simpler words, E = predicted Cloze percentage = 141.8401 − (0.214590 × number of characters) + (1.079812 * S), G = grade level, I = Flesch Index Score, R = the number of words ≤ 2 syllables, S = number of sentences, SMOG = Simple Measure of Gobbledygook, W = number of words. The readability levels of nursing care plan texts generated by ChatGPT-4.0 for specific nursing diagnoses were examined. As a result of this analysis, the median Gunning Fog (GFOG) value was found to be 17.34 (ranging from 13.38 to 24.28), and the median Flesch Reading Ease Score (FRES) was 30.69 (ranging from 9.30 to 53.25). Additionally, the median SMOG and Flesch-Kincaid Grade Level (FKGL) values indicated an approximate education level of 14.20 (ranging from 11.24 to 18.97) and 14.27 (ranging from 11.20 to 20.58) years, respectively. Similarly, the Automated Readability Index (ARI) value was 15.78 (ranging from 12.32 to 22.22), and the Coleman-Liau Index (CLI) value was 14.72 (ranging from 10.63 to 22.21), corresponding to years of education (Table 4 ) Table 3 Comparison of the Readability Scores of All Texts According to Text Content Using Calculator 1 and 2 with the 6th Grade Reading Level ( https://readabilityformulas.com/free-readability-formula-tests.php ). Calculator 1 Calculator 2 Calculator 1–2 statistics ChatGPT 4.0 Chat GPT C6thGRL ( P )*1 ChatGPT 4.0 Chat GPT 4.0 C6thGRL ( P )*1 FRES 31.35 (9–55) < .001 29.73 < .001 GFOG 17.75 (13.40–25.65) < .001 (9.49–51.29) FKGL 14.29 (11.74–20.72) < .001 16.53 < .001 CLI 15.17 (12.37–21.42) < .001 (13.22–25.16) < .001 SMOG 12.85 (9.57–18.09) < .001 13.78 < .001 ARI 16.52 (12.58–23.45) < .001 (11.25–20.04) < .001 LW 16.27 (12.44–22.38) < .001 Grade level 15.00 (12.00–20.00) < .001 Reading level n (%) Difficult to read 1 (4.6) Very difficult to read 3 (12.6) Extremally difficult to read 13 (58.1) Professional 5 (23.7) Somewhat difficult 0 (0) Readers age n (%) 8–9 years old (Fourth and Fifth graders) 0 (0) 10–11 years old (Fifth and Sixth graders) 0 (0) 11–13 years old (Sixth and Seventh graders) 0 (0) 12–14 years old (Seventh and Eighth graders) 0 (0) 13–15 years old (Eighth and Ninth graders) 0 (0) 14–15 years old (Ninth to Tenth graders) 0 (0) 15–17 years old (Tenth to Eleventh graders) n (%) 0 (0) 17–18 years old (Twelfth graders) 1 (4.5) 18–19 years old (college level entry) 3 (13.6) 21–22 years old (college level) 5 (22.7) 23 + years old 13 (59.1) College graduate 0 (0) P values in bold are statistically significant. ARI = Automated Readability Index, CLI = Coleman-Liau Index, FKGL = Flesch-Kincaid Grade Level, FRES = Flesch reading ease score, GFPG = Gunning FOG, LW = Linsear Write, SMOG = Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test. Table 4 Analysis of text complexity using the average values from Calculator 1 and Calculator 2 for all readability metrics and for comparison purposes. Readability indexes ChatGPT* Chat GPT C6thGRL (P)*1 FRES 30.69 (9.30–53.25) < .001 GFOG 17.34 (13.38–24.28) < .001 FKGL 14.27 (11.20–20.48) < .001 CLI 14.10 (11.24–18.97) < .001 SMOG 14.82 (10.83–22.41) < .001 ARI 15.78 (12.32–22.22) < .001 Calculator 1: https://readabilityformulas.com/free-readability-formula-tests.php Calculator 2: https://www.online-utility.org/english/readability_test_and_improve.jsp P values in bold are statistically significant. ARI = Automated Readability Index, CLI = Coleman-Liau Index, FKGL = Flesch-Kincaid Grade Level, FRES = Flesch reading ease score, GFPG = Gunning FOG, LW = Linsear Write, SMOG = Simple Measure of Gobbledygook. *[(Calculator 1) + (Calculator 2)]/2. C6thGRL ( P ) : Comparison of the responses according to 6th grade reading level ( P ). 1 Wilcoxon test. 3.3. Reliability and Quality Assessment The quality and content of the texts were evaluated using the DISCERN tool. As a result of this evaluation, the reliability levels of the texts, the quality of information they provided regarding nursing care, and their overall quality were found to be at a moderate level (see Table 2 ). Upon detailed examination, it was determined that all 50 ChatGPT-generated texts (100%) had moderate reliability, and 48 of them (96%) provided moderate-quality information about nursing care. Regarding the overall quality of the texts, only 3 texts (6%) were rated as good quality, 37 texts (74%) as moderate quality, and 8 texts (20%) as low quality(Table 5).A significant and positive correlation was found between the number of accessible references and the publication reliability (r = 0.428, p = .009) and total score (r = 0.394, p = .018) subdimensions of the DISCERN scale (P < .05). Similarly, the number of accessible article references was also found to be significantly correlated with publication reliability (r = 0.332, p = .024) (P < .05) (Table 6 ). Table 6 Correlation Analysis Between Descriptive Criteria and DISCERN Tablo 5. Average Scores of DISCERN Total Scale and Subscales and Evaluation of All Texts Using DISCERN DISCERN Bölümler Min-Max Ort ± SD Poor (Score 79%) Fair (Score 40%-79%) Good (Score > 79%) S1 - S8 Yayının güvenilirliği* 21–35 26.62 ± 2.85 0 (0) 50 (100) 0 (0) S9 ila S15 Hemşirelik bakımına ilişkin bilgi kalitesi** 13–36 27.22 ± 4,65 2 ( 4 ) 48 (96) 0 (0) S16 Genel kalitesi*** 3–5 3.56 ± 0.6 10 ( 20 ) 37 (74) 3 ( 6 ) Total**** 40–69 57,41 ± 5,9 * Minimum-maximum scores for reliability of publication range from 8 to 40 points.** Minimum-maximum scores for quality of information on nursing care range from 7 to 35 points.*** Minimum-maximum scores for overall quality range from 1 to 5 points.**** dMinimum-maximum scores of DISCERN tool range from 16 to 80 points Table 6. Correlation Analysis Between Descriptive Criteria and DISCERN Descriptive Criteria Reliability of Publication Quality Information on Nursing Care Overall Quality DISCERN, Total Number of references r 0.272 −0.023 −0.129 0.134 P .103 .828 .453 .410 Number of article references r 0.005 −0.122 −0.073 −0.040 P .985 .677 .751 .892 Number of book references r 0.180 0.029 −0.125 0.051 P .378 .920 .568 .793 Number of web references r 0.209 0.120 0.129 0.160 P .196 .459 .419 .323 Word count r 0.102 −0.073 0.395 0.021 P .741 .756 .182 .946 Paragraph count r 0.024 0.123 −0.061 0.092 P .886 .527 .758 .571 Similarity rate r 0.232 −0.135 0.096 0.068 P .211 .405 .565 .675 Number of accessible references r 0.428 0.110 0.221 0.39 P .009 * .479 .161 .019 * Number of accessible article references r 0.332 0.072 0.282 0.299 P .024 * .647 .087 .061 Number of accessible book references r 0.175 0.141 0.029 0.239 P .309 .419 .838 .145 Number of accessible web references r 0.298 0.155 0.126 0.278 P .064 .363 .479 .083 *P < .05; r: Spearman. 3.7. Intraclass Correlation Coefficients (ICCE) GFOG, FRES, CL, FKGL, ARI, and SMOG scores were calculated using two different calculators ( https://www.online-utility.org/english/readability_test_and_improve.jsp , https://readabilityformulas.com/free-readability-formula-tests.php ). 3.8. ICCE for ChatGPT The intraclass correlation coefficients were 0.972 for FRES, 0.963 for FKGL, 0.943 for GFOG, 0.980 for CL, 0.932 for ARI, and 0.950 for SMOG. 4. Discussion In this study, the readability, quality, and reliability levels of nursing care plan texts generated by ChatGPT were evaluated. The findings revealed that despite the increasing use of artificial intelligence-based texts in the field of healthcare, such texts still possess certain limitations. For health-related texts to be effective, they must be easily understandable by the target audience. The U.S. National Institutes of Health recommends that health texts be written at a 6th-grade reading level. However, in this study, the readability level of the nursing care plans generated by ChatGPT was found to be significantly above this recommended level. To date, no studies in the literature have specifically examined the readability of patient care plans generated with ChatGPT. However, studies assessing the readability of AI-generated texts in other health contexts support these findings. Özduran et al. (2025) found that responses generated by ChatGPT, Gemini, and Perplexity to questions about pain were difficult to read, and their reliability and quality levels were low ( 30 ). Kim et al. (2023) reported that the responses of chatbots such as ChatGPT, Bard, and Bing regarding concepts in supportive care were at an average 10th-grade level, which may pose challenges for individuals with low health literacy ( 31 ). Gül et al. (2023) examined responses generated by ChatGPT, Bard, and Perplexity to 100 questions about subdural hematoma and found that the readability levels exceeded the recommended standards ( 32 ). In addition, Haver et al. (2023) reported that most of the answers given to 25 questions about lung cancer were rated as “difficult” even for individuals above the 8th-grade level ( 33 ). These findings are consistent with the results of our study, which identified that ChatGPT texts require a higher level of reading proficiency than recommended. The readability of texts produced for nurses using artificial intelligence tools is of great importance. Complex or difficult-to-understand expressions in these texts may hinder nurses and nursing students from effectively understanding and applying patient care plans. Furthermore, clearly written and comprehensible content provided by artificial intelligence tools can significantly support nursing students in the process of preparing patient care plans. On the other hand, the use of overly complex texts may hinder patients’ active participation in their own healthcare processes and negatively impact treatment adherence. Therefore, the production of simple, clear, and literacy-appropriate supportive texts by artificial intelligence is a critical factor in enhancing the quality of patient care and the success of recovery processes. The quality of a text is directly related to the presence of structured information, clinically appropriate content, and measurable goals. In this study, it was observed that the care plans generated by ChatGPT were limited to general statements and that some of the goals were not aligned with clinical criteria. Similarly, in the literature, Woodnutt et al. (2023) reported that the care goals created by ChatGPT were often vague, unmeasurable, and lacked specificity ( 34 ). Lee et al. (2023) examined the responses given by ChatGPT and Gemini to questions about hypertension education and found that although both platforms demonstrated high information accuracy, the content was complex and required college-level literacy ( 35 ). In other words, a high level of accuracy does not automatically translate into high quality. The verifiability of the information provided by AI systems is of great importance. In our study, it was found that only 25% of the sources presented by ChatGPT were verifiable. This finding aligns with the widely reported problem of "hallucinated citations" in the literature. Ariyaratne et al. (2023) noted that a significant portion of the references used in five articles generated by ChatGPT were fabricated ( 36 ). Similarly, Gao and Xue (2023) reported that ChatGPT provided superficial and incomplete information in medical conversations ( 37 ). Musheyev et al. (2023), in their analysis of responses from ChatGPT and similar systems on urological topics, emphasized that DISCERN quality scores ranged between 2 and 5, and that the overall information quality was generally at a “moderate” level (Musheyev et al., 2023). These findings are consistent with the DISCERN scores observed in our study: ChatGPT was found to provide a moderate level of reliability, as it included current sources and authors in its responses. Artificial intelligence technologies, including ChatGPT, hold great potential in assisting with foundational research. However, many studies present conflicting opinions regarding the accuracy of the information generated by ChatGPT. Studies on ChatGPT’s ability to produce academic content reveal significant deficiencies. In one such study, the accuracy and quality of five articles written by ChatGPT were compared with published articles and were rated as “insufficient” ( 36 ). In another study, abstracts prepared by ChatGPT were found to contain vague and superficial expressions compared to the original article abstracts ( 4 ). Xue and colleagues also reported that ChatGPT lacked comprehensive and adequate knowledge in medical conversations ( 37 ). In line with these findings from the literature, this study also determined that ChatGPT texts offer only moderate reliability. Based on this result, it can be stated that the information provided by ChatGPT is not a sufficient standalone source for patient care and treatment practices by nurses and healthcare professionals, and should not be used without careful evaluation. In this study, one of the major concerns in academic research—high similarity rates—was examined specifically for ChatGPT-generated texts using iThenticate and Turnitin software. The findings revealed that the similarity rates in the analyzed texts were remarkably low. However, Turnitin’s artificial intelligence detection procedure reported that 100% of the ChatGPT-generated texts included in the study were identified as AI-generated. Previous research has shown that artificial intelligence can play a supportive role for healthcare professionals in a wide range of areas—from disease diagnosis to care planning, from outcome prediction to patient management ( 38 ). Moreover, it has been emphasized that the influence of AI is not limited to professionals, but can also shape patients’ healthcare-related decisions ( 39 ). In contrast to other research methodologies, the current study aimed to evaluate the readability, content quality, and reliability of patient care plan texts generated by artificial intelligence chatbots—rather than using traditional web-based sources. This represents a significant step toward understanding the potential direct impact of chatbots in patient care. AI tools offer considerable potential in terms of rapid access to information, text generation, and clinical support. However, in their current state, these technologies are not sufficient on their own for medical decision-making processes. In particular, they present serious limitations when it comes to preparing personalized, patient-specific, and empathetic care plans ( 34 ). Additionally, users must exercise caution regarding ethical concerns, privacy, and data security. Future studies should focus on improving AI systems by ensuring real-time access to up-to-date medical knowledge, supporting them with verifiable sources, and enhancing their ability to generate multilingual content suitable for users with low health literacy. Although AI holds great potential in healthcare, it is evident that in critical areas such as patient care planning, human supervision and expertise remain indispensable. This research may contribute to the development of future AI systems that offer more current, understandable, and reliable health information. 4.1. Strength of the Study Our study differs from other research by evaluating the readability, quality, and reliability levels of patient care plan texts. To standardize the readability assessment, two different calculators were used instead of a single tool. In addition, the study also aimed to examine the compatibility between these calculators. 4.2. Limitations of the Study Our study has several limitations. First, the research focused solely on ChatGPT due to its popularity, ease of use, and accessibility. Future studies that include other chatbots will offer more comprehensive insights into the overall readability, reliability, and quality of artificial intelligence applications. Second, our analysis is limited to responses generated on June 20, 2025; it is evident that responses obtained on a different date may alter the results of the study. Lastly, despite ChatGPT’s theoretical ability to generate unlimited text, practical constraints led us to analyze a representative sample consisting of only 50 English-language texts. These methodological limitations naturally impact the generalizability and scope of our findings. 5. Conclusion This research revealed that although ChatGPT can generate original texts and its information reliability improves as its data sources expand, the overall quality and reliability of the content remain moderate, and the texts require a certain level of literacy. This can hinder the comprehension of care plans by various stakeholders such as physicians, nurses, patients, and caregivers. Even experienced professionals working in high-paced environments require concise and clear language. Therefore, every piece of information obtained from AI must be critically reviewed and validated by nurses and other healthcare professionals before being integrated into clinical decisions, in order to ensure patient safety. When this conscious approach is adopted, AI chatbots can serve as safe educational tools for nursing students and as productivity-enhancing aids for professionals. To fully realize this potential, different models of such systems should be compared, future systems should be trained on large and academic datasets, and expert supervision should be incorporated. It must be remembered that the ultimate goal of these technologies is not to replace the core values of nursing—human-centered care, critical thinking, and empathetic communication—but rather to strengthen these competencies and provide healthcare professionals with more time and resources. Abbreviations ARI Automated Readability Index CLI Coleman-Liau Index FKGL Flesch-Kincaid Grade Level FRES Flesch Reading Ease Score GFOG Gunning Fog Index GQS Global Quality Score LW Linsear Write SMOG Simple Measure of Gobbledygook. Declarations Author contributions Conceptualization: Mucahide G. Gokalp, Sebnem C.Yucel. Data curation: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, Rüveyda Kargı. Formal analysis: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, Rüveyda Kargı. Investigation: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır. Methodology: Mucahide Gokalp, Sebnem Yucel. Project administration: Mucahide G. Gokalp, Sebnem Yucel. Resources: Mucahide G. Gokalp, Zehra Cakır. Supervision: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, Rüveyda Kargı. Validation: Mucahide G. Gokalp. Visualization: Mucahide G. Gokalp. Writing – original draft: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, Rüveyda Kargı. Writing – review & editing: Sebnem Yucel, Zehra Cakır, Emrah Boyun, Rüveyda Kargı.4 Ethical Considerations The version of artificial intelligence used in this study was an openly accessible "free research preview" to the researchers, and therefore did not require ethics committee approval or institutional permission. Thanks to the accessible and verifiable nature of the data, the methods of using AI—such as selecting, randomizing, and listing the diagnoses—were clearly detailed in the study. The research was conducted in accordance with the principles of the Declaration of Helsinki. Competing interests The authors declare no competing interests. Author details 1 PhD, Assist. Prof. Dr; Department of Nursing, Faculty of Health Sciences, Amasya University, 05100 Amasya, Turkey.2 PhD, Prof. Dr; 2 Department of Fundamentals of Nursing, Faculty of Nursing, Ege University, Izmir, Turkey, 35100 Izmir, Turkey. 3 , Nursing, Merzifon Kara Mustafa Paşa State Hospıtal, Amasya, Turkey. 35100 Amasya, Turkey. 4 Nursing, Amasya Fine Arts High School, Amasya, Turkey. 35100 Amasya, Turkey. 5 Nursing, Sabuncuoğlu Şerefeddin Traınıng And Research Hospıtal, Amasya, Turkey. 35100 Amasya, Turkey. References Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165 . OpenAI. (2024). GPT-4 technical report . Retrieved from https://openai.com/research Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Christiano, P. (2022). Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155 . Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. medRxiv . https://doi.org/10.1101/2023.01.26.23284974 Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2 (2), e0000198 Borji, A. (2023). A categorical archive of ChatGPT failures. arXiv preprint arXiv:2302.03494 . Gordijn, B., & Have, H. T. (2023). ChatGPT: Evolution or revolution in academic publishing? Medicine, Health Care and Philosophy, 26 (1), 1–3. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 60 (2), 176–187. Kasneci, E., Sessler, K., Kübler, R., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103 , 102274. Stokel-Walker, C. (2023). AI and the integrity of academic assessment: Is ChatGPT the new cheat sheet? Nature, 613 (7945), 423–425. Smith, M., & Lee, H. (2023). Public perception of ChatGPT in education: A sentiment analysis of Twitter content. Computers & Education: Artificial Intelligence, 4 , 100127. Huh, S. (2023). Can ChatGPT pass medical licensing exams? A preliminary study using parasitology questions. Journal of Educational Evaluation for Health Professions, 20 , 1–7. Reed, J. (2023). Using ChatGPT in nursing education: Opportunities and caveats. Nurse Educator, 48 (2), 89–93. Chang, T. H., Huang, W. C., & Lin, C. Y. (2023). Enhancing clinical education with AI chatbots: A randomized controlled trial in nursing students. Nurse Education Today, 127 , 105805. Öztürk, H. (2022). Nursing students' competence in developing care plans: A review. Anatolian Journal of Nursing and Health Sciences, 25 (2), 157–166. Çelik, S., & Yıldırım, D. (2023). Teaching nursing care plans in nursing education: Student perspectives. Journal of Nursing Education and Research, 19 (1), 45–53. Siegel, A. F. (2016). Statistics and data analysis: An introduction (2nd ed.). Wiley. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Herdman, T. H., & Kamitsuru, S. (Eds.). (2021). NANDA International nursing diagnoses: Definitions and classification, 2021–2023 . Thieme Medical Publishers. Lohr, S. L. (2022). Sampling: Design and analysis (3rd ed.). Chapman and Hall/CRC. Smith, T. J., & Johnson, K. M. (2022). Evaluating AI interfaces for reproducibility in medical research. AI in Medicine, 126 , 102153. Chen, Y., & Li, M. (2023). Evaluating AI-generated references: A comparison of real and fabricated citations. AI & Society, 38 (3), 921–936. iThenticate. (2024). Plagiarism detection software . Retrieved from https://www.ithenticate.com Turnitin. (2024). Similarity and AI detection tool . Retrieved from https://www.turnitin.com Zamanian, M., & Heydari, P. (2012). Readability of texts: State of the art. Procedia - Social and Behavioral Sciences, 70 , 1218–1228. DuBay, W. H. (2004). The principles of readability . Costa Mesa, CA: Impact Information. Kher, A., Johnson, S., & Griffith, R. (2017). Readability assessment of online patient education materials. Journal of Community Hospital Internal Medicine Perspectives, 7 (1), 34–38. Charnock, D., Shepperd, S., Needham, G., & Gann, R. (1999). DISCERN: An instrument for judging the quality of written consumer health information on treatment choices. Journal of Epidemiology & Community Health, 53 (2), 105–111. Cerminara, G., De Rosa, M., & Palombi, L. (2021). Validating the DISCERN tool in the digital health context: A usability study. JMIR Public Health and Surveillance, 7 (4), e25990. Ozduran, E., Akkoc, I., Büyükçoban, S., Erkin, Y., & Hancı, V. (2025). Readability, reliability and quality of responses generated by ChatGPT, Gemini, and Perplexity for the most frequently asked questions about pain. Medicine, 104 (11), e41780. https://doi.org/10.1097/MD.0000000000041780 Kim, J., Lee, Y., & Kim, M. (2023). Readability of palliative care content generated by AI chatbots. Palliative Medicine Reports, 4 (1), 17–23. Gul, A., Yalcın, G., & Özdemir, Y. (2023). The adequacy of chatbots in producing medical information: The case of subdural hematoma. Turkish Neurosurgery Journal, 33 (2), 89–97. Haver, J. S., Li, H., & Liu, Y. (2023). Understanding lung cancer through AI-generated content: A readability study. Journal of Thoracic Oncology, 18 (2), 145–152. Woodnutt, T., Lane, R., & Johnson, B. (2023). Evaluating goal specificity in AI-generated care plans. Journal of Clinical Nursing, 32 (7–8), 1320–1332. Lee, H., Park, S., & Choi, M. (2023). Analyzing ChatGPT and Gemini responses to hypertension education queries. Journal of Medical Internet Research, 25 , e45739. Ariyaratne, D., Abeyratne, A. I., & Samarasinghe, S. (2023). Hallucinated citations in AI-generated articles: A cross-comparison of sources. Journal of Medical Informatics, 45 (2), 112–120. Gao, C., & Xue, X. (2023). Artificial intelligence hallucinations: Risks of misinformation in health communication. Health Informatics Journal, 29 (3), 1–11. Smith, M., & Lee, H. (2023). Public perception of ChatGPT in education: A sentiment analysis of Twitter content. Computers & Education: Artificial Intelligence, 4 , 100127. Jones, L., Brown, R., & Wang, Y. (2023). AI in healthcare: Patient empowerment or risk? BMJ Innovations, 9 (1), 1–5. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7237457","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505033787,"identity":"00fe0930-aded-46ce-b768-af9e329b89bd","order_by":0,"name":"Mucahide GOKCEN GOKALP","email":"data:image/png;base64,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","orcid":"","institution":"Amasya University","correspondingAuthor":true,"prefix":"","firstName":"Mucahide","middleName":"GOKCEN","lastName":"GOKALP","suffix":""},{"id":505033789,"identity":"8547d22a-46cb-4bcd-abd0-d37f00949c69","order_by":1,"name":"Sebnem CINAR YUCEL","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Sebnem","middleName":"CINAR","lastName":"YUCEL","suffix":""},{"id":505033790,"identity":"1dca3345-2263-42a4-827b-ec4cf5361259","order_by":2,"name":"Zehra CAKIR","email":"","orcid":"","institution":"MERZIFON STATE HOSPITAL","correspondingAuthor":false,"prefix":"","firstName":"Zehra","middleName":"","lastName":"CAKIR","suffix":""},{"id":505033791,"identity":"47dd3be0-7b6b-4e99-b2e9-f51a8d31585a","order_by":3,"name":"EMRAH BOYUN","email":"","orcid":"","institution":"AMASYA FINE ARTS HIGH SCHOOL, AMASYA, Turkey","correspondingAuthor":false,"prefix":"","firstName":"EMRAH","middleName":"","lastName":"BOYUN","suffix":""},{"id":505033793,"identity":"893ce99b-0b7f-4d80-8ad7-aec8ee52e3c9","order_by":4,"name":"RÜVEYDA KARGI","email":"","orcid":"","institution":"SABUNCUOGLU SEREFETTİN TRAINING AND RESEARCH HOSPITAL","correspondingAuthor":false,"prefix":"","firstName":"RÜVEYDA","middleName":"","lastName":"KARGI","suffix":""}],"badges":[],"createdAt":"2025-07-28 22:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7237457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7237457/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-025-04171-w","type":"published","date":"2025-11-27T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97178503,"identity":"05bbedaa-fbad-4430-90c6-2a3e9e4b1e25","added_by":"auto","created_at":"2025-12-01 16:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237457/v1/f7409b15-30b9-4245-92c8-858bbf081f57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eReadability, Reliability, and Quality of Nursing Care Plan Texts Generated by Chatgpt\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChatGPT is an artificial intelligence-based chatbot designed to generate human-like text and understand natural language commands. Developed by OpenAI, this model is one of the largest language models, with 175\u0026nbsp;billion parameters, and has marked a significant breakthrough in the field of natural language processing (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is transforming human-machine interactions across various domains, including education, healthcare, and customer service.\u003c/p\u003e\u003cp\u003eChatGPT is a multilingual language model trained on a diverse 570 GB text dataset (including books, articles, websites, etc.) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The model has been enhanced through reinforcement learning from human feedback (RLHF), enabling it to better understand user intent and generate coherent, fluent, and contextually appropriate responses aligned with human expectations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite ChatGPT's advanced language capabilities, it has significant limitations. In a study conducted by Gao et al. (2023), it was found that scientific article abstracts generated by the model were largely identifiable by AI detection tools, and 14% of these abstracts contained fabricated information (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, when faced with insufficient data, the model tends to generate inaccurate information and cite non-existent sources (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, the readability, reliability, and accuracy of texts generated by ChatGPT must be carefully evaluated, especially in academic contexts. Borji (2023) points out that ChatGPT can produce inconsistent, biased, or contextually irrelevant responses and frequently suffers from the problem of \"hallucination\" (fabricated content) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This raises concerns about the model\u0026rsquo;s reliability and emphasizes the importance of using it cautiously and under supervision as a supportive tool. Among the limitations of ChatGPT are its inability to process data beyond June 2024, its tendency to repeat citations, and its particular unreliability in compiling medical information accurately (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These factors restrict the model\u0026rsquo;s usability in fields such as healthcare, which require continuously updated information.\u003c/p\u003e\u003cp\u003eAccording to an evaluation by Gordijn and Have (2023), although ChatGPT\u0026rsquo;s texts are linguistically fluent, they often contain repetitive content and lack academic depth (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, it has been stated that ChatGPT is not reliable in terms of scientific integrity and cannot replace human researchers (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, Borji (2023) noted that the model falls short in meeting essential academic writing requirements such as originality, critical analysis, and source verification (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These findings indicate that ChatGPT should only be considered a supportive tool in academic writing and must be used under human supervision.\u003c/p\u003e\u003cp\u003eThe release of ChatGPT in 2022 sparked debates about its impact on writing assignments in academic education. There are concerns that students might use this tool unethically for completing assignments and projects (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The existing literature shows that ChatGPT has become a popular tool among undergraduate and graduate students for producing academic texts and assignments (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This situation has led to new discussions about how artificial intelligence tools should be evaluated in the context of academic integrity.\u003c/p\u003e\u003cp\u003eStokel-Walker (2023) stated that ChatGPT is capable of writing referenced articles and providing consistent answers to exam-type questions, which increases the risk of plagiarism among students. Academics are concerned about the impact of such tools on academic assessments (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A study by Smith and Lee (2023) analyzing 10,732 tweets confirmed concerns about the potential misuse of ChatGPT and its negative effects on education (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, a study conducted by Huh (2023) in South Korea found that although ChatGPT performed worse than medical students, it provided acceptable answers on a parasitology exam (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These findings highlight both the potential and limitations of artificial intelligence models in education.\u003c/p\u003e\u003cp\u003eChatGPT provides significant contributions to nursing educators in preparing course content, educational materials, and case scenarios (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It has been noted that AI-supported visuals enhance visual learning and reflection across different nursing education settings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In a study by Chang et al. (2023), nursing students who received physical examination training through an AI-based chatbot demonstrated improvements in academic achievement, critical thinking skills, and motivation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, ChatGPT\u0026rsquo;s ability to quickly compile patient information, generate comprehensive care plans, and detail nursing interventions contributes to time savings and increased efficiency in care planning (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). At this point, the readability level of AI-supported nursing care plans is a key factor determining the effectiveness of information transfer among various stakeholders, including clinicians (doctors, nurses), learners (nursing students), and service recipients (patients, caregivers). Even experienced professionals working in busy clinical practice environments require concise and clear language to process information quickly and accurately, reinforcing the scientific and practical importance of this issue. These capabilities make artificial intelligence a valuable tool in nursing education and care processes and encourage its use.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePurpose and Research Questions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The primary aim of this study is to evaluate the readability, reliability, quality, and originality of nursing care plans generated by artificial intelligence. In nursing education, students are assigned care plan tasks in various clinical fields; these plans form the foundation of nursing practice (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Within this context, the study seeks to answer the following questions:\u003c/p\u003e\u003cp\u003eRegarding nursing care plan texts;\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIs their readability at the recommended 6th-grade reading level?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat are the quality scores?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow are the reliability subscale values evaluated?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow is the level of originality in terms of similarity rates?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDo the text length (word count), the number of cited sources, and originality (similarity rate) affect how quality and reliable the nursing care plan texts written by ChatGPT are?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Research Design and Sample\u003c/h2\u003e\u003cp\u003eThis study aimed to evaluate texts generated by ChatGPT. The sample of the study was determined based on the Central Limit Theorem. This theorem states that, with a sufficiently large sample size, the average of many random variables tends to approximate a normal distribution, allowing sample statistics to reliably estimate population parameters (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the Central Limit Theorem, sample sizes larger than 30 increase the power to represent the population (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this study, to further enhance the generalizability and representativeness of the results, a sample size of 50 was chosen instead of the minimum recommended 30. Accordingly, 50 different nursing-related texts generated by ChatGPT were analyzed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Selection of Topics\u003c/h2\u003e\u003cp\u003eIn this study, the topics for the texts to be generated by ChatGPT were selected from the 2021\u0026ndash;2023 taxonomy of the North American Nursing Diagnosis Association (NANDA) International, which includes 267 different nursing diagnoses (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAfter numbering the 267 nursing diagnoses, the researchers created a randomization table using the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.random.org\u003c/span\u003e\u003cspan address=\"http://www.random.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (see Supplementary Digital Content, Randomization table.docx). Through this table, 50 nursing diagnoses were selected using a simple random sampling method. This method ensures that each diagnosis has an equal chance of being selected, guaranteeing the impartiality of the selection process (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data Collection\u003c/h2\u003e\u003cp\u003eThis study followed a rigorous data collection process to evaluate ChatGPT\u0026rsquo;s ability to generate texts that meet academic standards. Data were collected on June 20, 2025, using the ChatGPT web interface (June 20 version) available at ai.com. For transparency and reproducibility, access was made via Google Chrome (version 109.0.5414.119) running on Windows; these details are important for verifying the findings (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin the scope of the study, for each randomly selected nursing diagnosis, ChatGPT was prompted with the instruction: \u0026ldquo;Please write a text related to -NANDA DIAGNOSIS- and a nursing care plan. Use the maximum number of references and include citations within the text.\u0026rdquo; The purpose of this prompt was not only to generate information but also to assess ChatGPT\u0026rsquo;s ability to integrate academic references and provide citations. The phrase \u0026ldquo;nursing care plan\u0026rdquo; was specifically emphasized to ensure that the content was structured and clinically oriented. The capacity of AI models to generate texts with academic references is a significant topic in current research (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll texts generated by ChatGPT were securely saved by the researchers for detailed evaluation and analysis in subsequent stages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data Collection Tools\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Descriptive Information Assessment\u003c/h2\u003e\u003cp\u003eThe descriptive information form consisted of 12 questions covering word and paragraph count, similarity rate (using iThenticate and Turnitin software; iThenticate, 2024; Turnitin, 2024), number of references, publication year, type, and accessibility of the texts (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Readability Assessment\u003c/h2\u003e\u003cp\u003eThe readability of nursing care plan texts generated by ChatGPT was analyzed using two different online tools with evaluation capabilities. These tools were ReadabilityFormulas.com (Calculator 1) and Online-Utility.org (Calculator 2), each employing different readability formulas to assess the texts (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The analysis utilized seven different readability formulas: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFOG), Simple Measure of Gobbledygook (SMOG), Automated Readability Index (ARI), Coleman-Liau Index (CLI), and Linsear Write (LW). Using these formulas, the readability score of each text was calculated, and the results were reported as median (minimum\u0026ndash;maximum) values. The accepted readability level was set at 80 or above for the FRES formula, and 6 or above for the other six formulas (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe readability, quality, and reliability of the texts were independently evaluated by two nursing experts (M.G. and Z.C.). Final scores were determined by averaging the results of both researchers. Details and calculation logic of the seven different readability formulas used in the evaluation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3. Reliability and Quality Assessment\u003c/h2\u003e\u003cp\u003eDISCERN, developed through a collaboration between the British Library and the National Health Service (NHS), is a tool designed to measure the reliability of online health information (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This tool consists of three sections and 16 questions: the first eight questions assess reliability, the next seven evaluate the quality of information regarding treatment options, and the final question measures overall quality. Questions are scored from 1 to 5, resulting in a total score ranging between 16 and 80. According to the scoring, scores below 40% are considered poor, between 40% and 79% moderate, and 80% or above good quality (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using SPSS version 25 (IBM Corp., Armonk, New York). Descriptive analyses included frequencies, percentages, means, standard deviations, and ranges. The relationship between numerical variables and DISCERN scores was tested using Spearman correlation analysis. Turnitin and iThenticate programs were used to determine the similarity rates of the texts. In cases requiring comparison of continuous data, the Mann-Whitney U and Wilcoxon tests were applied. To assess the consistency of the calculators, intraclass correlation coefficient (ICC) analysis was performed for each formula. The reliability of the evaluation between two independent observers was determined by calculating Cohen\u0026rsquo;s kappa (κ) value. A p-value less than .05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe texts generated by ChatGPT based on randomly selected nursing diagnoses were thoroughly examined by two different researchers. A high level of agreement between the evaluators was found (Cohen\u0026rsquo;s Kappa coefficient κ: 0.827), indicating a high reliability of the texts. The average word count of the texts was 366.95\u0026thinsp;\u0026plusmn;\u0026thinsp;57.98, ranging from 402 to 698 words, showing that the texts were generally short.\u003c/p\u003e\u003cp\u003eIt was determined that most of the sources used in the texts were fictional and could not be found in academic search engines such as PubMed or Google. Nevertheless, similarity checks conducted with platforms like iThenticate and Turnitin revealed very low plagiarism rates. An important finding was that, according to analyses performed using Turnitin\u0026rsquo;s AI detection technology, 100% of all examined texts were confirmed to have been written by artificial intelligence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Information Form\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublication years of references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1984\u0026ndash;2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026thinsp;\u0026plusmn;\u0026thinsp;6,05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of accessible references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1,01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of web references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of accessible web references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of article references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of accessible article references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of book references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of accessible book references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e402\u0026ndash;698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e538\u0026thinsp;\u0026plusmn;\u0026thinsp;110.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParagraph count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eiThenticate similarity rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnitin similarity rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnitin AI rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Readability Assessment of ChatGPT Using Average Scores from Calculator 1 and 2\u003c/h2\u003e\u003cp\u003eWhen the average readability scores of the texts generated by ChatGPT were compared to the sixth-grade reading level standard, statistically significant differences were found across all measures (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). This clearly indicates that the readability of the texts is above the sixth-grade level. Additionally, statistically significant results were obtained when comparing the results of Calculator 1 and Calculator 2 as well as their averages (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2. ChatGPT-4.0 Analysis Outcomes Utilizing Average Results from Calculator 1 and 2\u003c/h2\u003e\u003cp\u003eThe readability levels of nursing care plan texts generated by ChatGPT-4.0 for specific nursing diagnoses were examined. The analysis revealed a median Gunning Fog (GFOG) score of 17.34 (range: 13.38\u0026ndash;24.28) and a median Flesch Reading Ease Score (FRES) of 30.69 (range: 9.30\u0026ndash;53.25). Furthermore, the median SMOG and Flesch-Kincaid Grade Level (FKGL) scores were 14.20 (range: 11.24\u0026ndash;18.97) and 14.27 (range: 11.20\u0026ndash;20.58), respectively, indicating an approximate education level of 14 years. Similarly, the Automated Readability Index (ARI) score was 15.78 (range: 12.32\u0026ndash;22.22), and the Coleman-Liau Index (CLI) score was 14.72 (range: 10.63\u0026ndash;22.21), corresponding to a comparable education level (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReadability tools, formulas and descriptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadability index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormula\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlesch Reading Ease Score(FRES)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt was created to assess the readability of newspapers and is particularly effective for evaluating school textbooks and technical manuals. This standardized test is utilized by numerous US government agencies. The scores range from 0 to 100, with higher scores indicating greater ease of reading.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI = (206.835 \u0026minus; (84.6 X (B/W)) \u0026minus; (1.015 X (W/S)))\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGunning FOG (GFOG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt was designed to assist American businesses in enhancing the readability of their written content and is applicable across various disciplines. It estimates the number of years of education required for a person to understand a given text.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u0026thinsp;=\u0026thinsp;0.4 X (W/S+((C*/W) X 100))\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Measure of\u003c/p\u003e\u003cp\u003eGobbledygook (SMOG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is typically appropriate for middle-aged readers, ranging from 4th grade to college level. While it aims to test 100% compre-hension, most formulas measure about 50%-75% comprehension. It is most accurate when applied to documents that are at least 30 sentences long. It measures the number of years of education the average person needs to understand a text.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u0026thinsp;=\u0026thinsp;1.0430X \u0026radic; C\u0026thinsp;+\u0026thinsp;3.1291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlesch\u0026ndash;Kincaid grade\u003c/p\u003e\u003cp\u003elevel (FKGL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePart of the Kincaid Navy Personnel test collection, it was designed for technical documentation and is suitable for a wide range of disciplines. Delineates the academic capacity level imperative for grasping the written material\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG = (11.8 X (B/W)) +\u003c/p\u003e\u003cp\u003e(0.39 X (W/S))\u0026thinsp;\u0026minus;\u0026thinsp;15.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutomated readability index (ARI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe ARI (Automated Readability Index) has been utilized by the military for writing technical manuals. Its calculation provides the grade level required to comprehend the text. Assesses the scholastic rank in American educational institutions needed to be capable of comprehending written material. The greater the number of characters, the more complex the term.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARI\u0026thinsp;=\u0026thinsp;4.71 X\u003c/p\u003e\u003cp\u003el\u0026thinsp;+\u0026thinsp;0.5*ASL\u0026thinsp;\u0026minus;\u0026thinsp;21.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColeman\u0026ndash;Liau (CL) score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is designed for middle-aged readers, spanning from 4th grade to college level. The formula is based on text with a grade level range of 0.4 to 16.3 and is applicable to many industries. Evaluates the educational level required for understanding a text and offers an associated grade level in the US education system.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG = (\u0026minus;\u0026thinsp;27.4004 X (E/100))\u0026thinsp;+\u0026thinsp;23.06395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinsear Write (LW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt was developed for the United States Air Force to assist in calculating the readability of their technical manuals. Offers an approximate assessment of the academic level needed to comprehend the text.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLW = (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;+\u0026thinsp;3C)/S\u003c/p\u003e\u003cp\u003eResult\u003c/p\u003e\u003cp\u003eIf\u0026thinsp;\u0026gt;\u0026thinsp;20, divide by 2\u003c/p\u003e\u003cp\u003eIf\u0026thinsp;\u0026le;\u0026thinsp;20, subtract 2, and then divide by 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eASL\u0026thinsp;=\u0026thinsp;the average number of sentences per 100 words, B\u0026thinsp;=\u0026thinsp;number of syllables, C\u0026thinsp;=\u0026thinsp;complex words (\u0026ge;\u0026thinsp;3 syllables), C* = complex words with exceptions including, proper nouns, words made 3 syllables by addition of \u0026ldquo;ed\u0026rdquo; or \u0026ldquo;es,\u0026rdquo; compound words made of simpler words, E\u0026thinsp;=\u0026thinsp;predicted Cloze percentage\u0026thinsp;=\u0026thinsp;141.8401 \u0026minus; (0.214590 \u0026times; number of characters) + (1.079812 * S), G\u0026thinsp;=\u0026thinsp;grade level, I\u0026thinsp;=\u0026thinsp;Flesch Index Score, R\u0026thinsp;=\u0026thinsp;the number of words\u0026thinsp;\u0026le;\u0026thinsp;2 syllables, S\u0026thinsp;=\u0026thinsp;number of sentences, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook, W\u0026thinsp;=\u0026thinsp;number of words.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe readability levels of nursing care plan texts generated by ChatGPT-4.0 for specific nursing diagnoses were examined. As a result of this analysis, the median Gunning Fog (GFOG) value was found to be 17.34 (ranging from 13.38 to 24.28), and the median Flesch Reading Ease Score (FRES) was 30.69 (ranging from 9.30 to 53.25). Additionally, the median SMOG and Flesch-Kincaid Grade Level (FKGL) values indicated an approximate education level of 14.20 (ranging from 11.24 to 18.97) and 14.27 (ranging from 11.20 to 20.58) years, respectively. Similarly, the Automated Readability Index (ARI) value was 15.78 (ranging from 12.32 to 22.22), and the Coleman-Liau Index (CLI) value was 14.72 (ranging from 10.63 to 22.21), corresponding to years of education (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the Readability Scores of All Texts According to Text Content Using Calculator 1 and 2 with the 6th Grade Reading Level (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCalculator 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCalculator 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalculator 1\u0026ndash;2 statistics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eChatGPT 4.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eChat GPT C6thGRL (\u003c/b\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e)*1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eChatGPT 4.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eChat GPT 4.0 C6thGRL (\u003c/b\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e)*1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.35 (9\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.75 (13.40\u0026ndash;25.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(9.49\u0026ndash;51.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFKGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.29 (11.74\u0026ndash;20.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.17 (12.37\u0026ndash;21.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.22\u0026ndash;25.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.85 (9.57\u0026ndash;18.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.52 (12.58\u0026ndash;23.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(11.25\u0026ndash;20.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.27 (12.44\u0026ndash;22.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (12.00\u0026ndash;20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReading level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery difficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremally difficult to read\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomewhat difficult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReaders age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;9 years old (Fourth and Fifth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;11 years old (Fifth and Sixth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;13 years old (Sixth and Seventh graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;14 years old (Seventh and Eighth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u0026ndash;15 years old (Eighth and Ninth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u0026ndash;15 years old (Ninth to Tenth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;17 years old (Tenth to Eleventh graders) n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u0026ndash;18 years old (Twelfth graders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;19 years old (college level entry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u0026ndash;22 years old (college level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u0026thinsp;+\u0026thinsp;years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eP values in bold are statistically significant. ARI\u0026thinsp;=\u0026thinsp;Automated Readability Index, CLI\u0026thinsp;=\u0026thinsp;Coleman-Liau Index, FKGL\u0026thinsp;=\u0026thinsp;Flesch-Kincaid Grade Level, FRES\u0026thinsp;=\u0026thinsp;Flesch reading ease score, GFPG\u0026thinsp;=\u0026thinsp;Gunning FOG, LW\u0026thinsp;=\u0026thinsp;Linsear Write, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook. *C6thGRL (P): Comparison of the responses according to 6th grade reading level (P). 1 Wilcoxon test.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of text complexity using the average values from Calculator 1 and Calculator 2 for all readability metrics and for comparison purposes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadability indexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChatGPT*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChat GPT C6thGRL (P)*1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.69 (9.30\u0026ndash;53.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.34 (13.38\u0026ndash;24.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFKGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.27 (11.20\u0026ndash;20.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.10 (11.24\u0026ndash;18.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.82 (10.83\u0026ndash;22.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.78 (12.32\u0026ndash;22.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCalculator 1: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eCalculator 2: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.online-utility.org/english/readability_test_and_improve.jsp\u003c/span\u003e\u003cspan address=\"https://www.online-utility.org/english/readability_test_and_improve.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values in bold are statistically significant.\u003c/p\u003e\u003cp\u003eARI\u0026thinsp;=\u0026thinsp;Automated Readability Index, CLI\u0026thinsp;=\u0026thinsp;Coleman-Liau Index, FKGL\u0026thinsp;=\u0026thinsp;Flesch-Kincaid Grade Level, FRES\u0026thinsp;=\u0026thinsp;Flesch reading ease score, GFPG\u0026thinsp;=\u0026thinsp;Gunning FOG, LW\u0026thinsp;=\u0026thinsp;Linsear Write, SMOG\u0026thinsp;=\u0026thinsp;Simple Measure of Gobbledygook.\u003c/p\u003e\u003cp\u003e*[(Calculator 1) + (Calculator 2)]/2.\u003c/p\u003e\u003cp\u003eC6thGRL (\u003cem\u003eP\u003c/em\u003e\u003cb\u003e)\u003c/b\u003e: Comparison of the responses according to 6th grade reading level (\u003cem\u003eP\u003c/em\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1 Wilcoxon test.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Reliability and Quality Assessment\u003c/h2\u003e\u003cp\u003eThe quality and content of the texts were evaluated using the DISCERN tool. As a result of this evaluation, the reliability levels of the texts, the quality of information they provided regarding nursing care, and their overall quality were found to be at a moderate level (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon detailed examination, it was determined that all 50 ChatGPT-generated texts (100%) had moderate reliability, and 48 of them (96%) provided moderate-quality information about nursing care. Regarding the overall quality of the texts, only 3 texts (6%) were rated as good quality, 37 texts (74%) as moderate quality, and 8 texts (20%) as low quality(Table\u0026nbsp;5).A significant and positive correlation was found between the number of accessible references and the publication reliability (r\u0026thinsp;=\u0026thinsp;0.428, p\u0026thinsp;=\u0026thinsp;.009) and total score (r\u0026thinsp;=\u0026thinsp;0.394, p\u0026thinsp;=\u0026thinsp;.018) subdimensions of the DISCERN scale (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). Similarly, the number of accessible article references was also found to be significantly correlated with publication reliability (r\u0026thinsp;=\u0026thinsp;0.332, p\u0026thinsp;=\u0026thinsp;.024) (P\u0026thinsp;\u0026lt;\u0026thinsp;.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Analysis Between Descriptive Criteria and DISCERN\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eTablo 5. Average Scores of DISCERN Total Scale and Subscales and Evaluation of All Texts Using DISCERN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDISCERN B\u0026ouml;l\u0026uuml;mler\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOrt\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoor (Score 79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFair (Score 40%-79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood (Score\u0026thinsp;\u0026gt;\u0026thinsp;79%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1 - S8 Yayının g\u0026uuml;venilirliği*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS9 ila S15 Hemşirelik bakımına ilişkin bilgi kalitesi**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS16 Genel kalitesi***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal****\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57,41\u0026thinsp;\u0026plusmn;\u0026thinsp;5,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e* Minimum-maximum scores for reliability of publication range from 8 to 40 points.** Minimum-maximum scores for quality of information on nursing care range from 7 to 35 points.*** Minimum-maximum scores for overall quality range from 1 to 5 points.**** dMinimum-maximum scores of DISCERN tool range from 16 to 80 points\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 6. Correlation Analysis Between Descriptive Criteria and DISCERN\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescriptive Criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReliability of Publication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuality Information on Nursing Care\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverall Quality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDISCERN,\u003c/p\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of article references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of book references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of web references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWord count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParagraph count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSimilarity rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of accessible references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of accessible article references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of accessible book references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of accessible web references\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003e*P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003e.05;\u003c/em\u003e r: \u003cem\u003eSpearman.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Intraclass Correlation Coefficients (ICCE)\u003c/h2\u003e\u003cp\u003eGFOG, FRES, CL, FKGL, ARI, and SMOG scores were calculated using two different calculators (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.online-utility.org/english/readability_test_and_improve.jsp\u003c/span\u003e\u003cspan address=\"https://www.online-utility.org/english/readability_test_and_improve.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://readabilityformulas.com/free-readability-formula-tests.php\u003c/span\u003e\u003cspan address=\"https://readabilityformulas.com/free-readability-formula-tests.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.8. ICCE for ChatGPT\u003c/h2\u003e\u003cp\u003eThe intraclass correlation coefficients were 0.972 for FRES, 0.963 for FKGL, 0.943 for GFOG, 0.980 for CL, 0.932 for ARI, and 0.950 for SMOG.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, the readability, quality, and reliability levels of nursing care plan texts generated by ChatGPT were evaluated. The findings revealed that despite the increasing use of artificial intelligence-based texts in the field of healthcare, such texts still possess certain limitations. For health-related texts to be effective, they must be easily understandable by the target audience. The U.S. National Institutes of Health recommends that health texts be written at a 6th-grade reading level. However, in this study, the readability level of the nursing care plans generated by ChatGPT was found to be significantly above this recommended level.\u003c/p\u003e\u003cp\u003eTo date, no studies in the literature have specifically examined the readability of patient care plans generated with ChatGPT. However, studies assessing the readability of AI-generated texts in other health contexts support these findings. \u0026Ouml;zduran et al. (2025) found that responses generated by ChatGPT, Gemini, and Perplexity to questions about pain were difficult to read, and their reliability and quality levels were low (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Kim et al. (2023) reported that the responses of chatbots such as ChatGPT, Bard, and Bing regarding concepts in supportive care were at an average 10th-grade level, which may pose challenges for individuals with low health literacy (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). G\u0026uuml;l et al. (2023) examined responses generated by ChatGPT, Bard, and Perplexity to 100 questions about subdural hematoma and found that the readability levels exceeded the recommended standards (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, Haver et al. (2023) reported that most of the answers given to 25 questions about lung cancer were rated as \u0026ldquo;difficult\u0026rdquo; even for individuals above the 8th-grade level (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings are consistent with the results of our study, which identified that ChatGPT texts require a higher level of reading proficiency than recommended. The readability of texts produced for nurses using artificial intelligence tools is of great importance. Complex or difficult-to-understand expressions in these texts may hinder nurses and nursing students from effectively understanding and applying patient care plans. Furthermore, clearly written and comprehensible content provided by artificial intelligence tools can significantly support nursing students in the process of preparing patient care plans. On the other hand, the use of overly complex texts may hinder patients\u0026rsquo; active participation in their own healthcare processes and negatively impact treatment adherence. Therefore, the production of simple, clear, and literacy-appropriate supportive texts by artificial intelligence is a critical factor in enhancing the quality of patient care and the success of recovery processes.\u003c/p\u003e\u003cp\u003eThe quality of a text is directly related to the presence of structured information, clinically appropriate content, and measurable goals. In this study, it was observed that the care plans generated by ChatGPT were limited to general statements and that some of the goals were not aligned with clinical criteria. Similarly, in the literature, Woodnutt et al. (2023) reported that the care goals created by ChatGPT were often vague, unmeasurable, and lacked specificity (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Lee et al. (2023) examined the responses given by ChatGPT and Gemini to questions about hypertension education and found that although both platforms demonstrated high information accuracy, the content was complex and required college-level literacy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In other words, a high level of accuracy does not automatically translate into high quality.\u003c/p\u003e\u003cp\u003eThe verifiability of the information provided by AI systems is of great importance. In our study, it was found that only 25% of the sources presented by ChatGPT were verifiable. This finding aligns with the widely reported problem of \"hallucinated citations\" in the literature. Ariyaratne et al. (2023) noted that a significant portion of the references used in five articles generated by ChatGPT were fabricated (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Similarly, Gao and Xue (2023) reported that ChatGPT provided superficial and incomplete information in medical conversations (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Musheyev et al. (2023), in their analysis of responses from ChatGPT and similar systems on urological topics, emphasized that DISCERN quality scores ranged between 2 and 5, and that the overall information quality was generally at a \u0026ldquo;moderate\u0026rdquo; level (Musheyev et al., 2023). These findings are consistent with the DISCERN scores observed in our study: ChatGPT was found to provide a moderate level of reliability, as it included current sources and authors in its responses.\u003c/p\u003e\u003cp\u003eArtificial intelligence technologies, including ChatGPT, hold great potential in assisting with foundational research. However, many studies present conflicting opinions regarding the accuracy of the information generated by ChatGPT. Studies on ChatGPT\u0026rsquo;s ability to produce academic content reveal significant deficiencies. In one such study, the accuracy and quality of five articles written by ChatGPT were compared with published articles and were rated as \u0026ldquo;insufficient\u0026rdquo; (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In another study, abstracts prepared by ChatGPT were found to contain vague and superficial expressions compared to the original article abstracts (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Xue and colleagues also reported that ChatGPT lacked comprehensive and adequate knowledge in medical conversations (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn line with these findings from the literature, this study also determined that ChatGPT texts offer only moderate reliability. Based on this result, it can be stated that the information provided by ChatGPT is not a sufficient standalone source for patient care and treatment practices by nurses and healthcare professionals, and should not be used without careful evaluation.\u003c/p\u003e\u003cp\u003eIn this study, one of the major concerns in academic research\u0026mdash;high similarity rates\u0026mdash;was examined specifically for ChatGPT-generated texts using iThenticate and Turnitin software. The findings revealed that the similarity rates in the analyzed texts were remarkably low. However, Turnitin\u0026rsquo;s artificial intelligence detection procedure reported that 100% of the ChatGPT-generated texts included in the study were identified as AI-generated.\u003c/p\u003e\u003cp\u003ePrevious research has shown that artificial intelligence can play a supportive role for healthcare professionals in a wide range of areas\u0026mdash;from disease diagnosis to care planning, from outcome prediction to patient management (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Moreover, it has been emphasized that the influence of AI is not limited to professionals, but can also shape patients\u0026rsquo; healthcare-related decisions (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In contrast to other research methodologies, the current study aimed to evaluate the readability, content quality, and reliability of patient care plan texts generated by artificial intelligence chatbots\u0026mdash;rather than using traditional web-based sources. This represents a significant step toward understanding the potential direct impact of chatbots in patient care.\u003c/p\u003e\u003cp\u003eAI tools offer considerable potential in terms of rapid access to information, text generation, and clinical support. However, in their current state, these technologies are not sufficient on their own for medical decision-making processes. In particular, they present serious limitations when it comes to preparing personalized, patient-specific, and empathetic care plans (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Additionally, users must exercise caution regarding ethical concerns, privacy, and data security. Future studies should focus on improving AI systems by ensuring real-time access to up-to-date medical knowledge, supporting them with verifiable sources, and enhancing their ability to generate multilingual content suitable for users with low health literacy.\u003c/p\u003e\u003cp\u003eAlthough AI holds great potential in healthcare, it is evident that in critical areas such as patient care planning, human supervision and expertise remain indispensable. This research may contribute to the development of future AI systems that offer more current, understandable, and reliable health information.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Strength of the Study\u003c/h2\u003e\u003cp\u003eOur study differs from other research by evaluating the readability, quality, and reliability levels of patient care plan texts. To standardize the readability assessment, two different calculators were used instead of a single tool. In addition, the study also aimed to examine the compatibility between these calculators.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Limitations of the Study\u003c/h2\u003e\u003cp\u003eOur study has several limitations. First, the research focused solely on ChatGPT due to its popularity, ease of use, and accessibility. Future studies that include other chatbots will offer more comprehensive insights into the overall readability, reliability, and quality of artificial intelligence applications. Second, our analysis is limited to responses generated on June 20, 2025; it is evident that responses obtained on a different date may alter the results of the study. Lastly, despite ChatGPT\u0026rsquo;s theoretical ability to generate unlimited text, practical constraints led us to analyze a representative sample consisting of only 50 English-language texts. These methodological limitations naturally impact the generalizability and scope of our findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research revealed that although ChatGPT can generate original texts and its information reliability improves as its data sources expand, the overall quality and reliability of the content remain moderate, and the texts require a certain level of literacy. This can hinder the comprehension of care plans by various stakeholders such as physicians, nurses, patients, and caregivers. Even experienced professionals working in high-paced environments require concise and clear language. Therefore, every piece of information obtained from AI must be critically reviewed and validated by nurses and other healthcare professionals before being integrated into clinical decisions, in order to ensure patient safety. When this conscious approach is adopted, AI chatbots can serve as safe educational tools for nursing students and as productivity-enhancing aids for professionals.\u003c/p\u003e\u003cp\u003eTo fully realize this potential, different models of such systems should be compared, future systems should be trained on large and academic datasets, and expert supervision should be incorporated. It must be remembered that the ultimate goal of these technologies is not to replace the core values of nursing\u0026mdash;human-centered care, critical thinking, and empathetic communication\u0026mdash;but rather to strengthen these competencies and provide healthcare professionals with more time and resources.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAutomated Readability Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCLI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColeman-Liau Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFKGL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFlesch-Kincaid Grade Level\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFRES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFlesch Reading Ease Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGFOG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGunning Fog Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGQS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Quality Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinsear Write\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMOG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSimple Measure of Gobbledygook.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Mucahide G. Gokalp, Sebnem C.Yucel. Data curation: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, R\u0026uuml;veyda Kargı. Formal analysis: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, R\u0026uuml;veyda Kargı. Investigation: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır. Methodology: Mucahide Gokalp, Sebnem Yucel. Project administration: Mucahide G. Gokalp, Sebnem Yucel. Resources: Mucahide G. Gokalp, Zehra Cakır. Supervision: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, R\u0026uuml;veyda Kargı. Validation: Mucahide G. Gokalp. Visualization: Mucahide G. Gokalp. Writing \u0026ndash; original draft: Mucahide G. Gokalp, Sebnem Yucel, Zehra Cakır, Emrah Boyun, R\u0026uuml;veyda Kargı. Writing \u0026ndash; review \u0026amp; editing: Sebnem Yucel, Zehra Cakır, Emrah Boyun, R\u0026uuml;veyda Kargı.4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe version of artificial intelligence used in this study was an openly accessible \u0026quot;free research preview\u0026quot; to the researchers, and therefore did not require ethics committee approval or institutional permission. Thanks to the accessible and verifiable nature of the data, the methods of using AI\u0026mdash;such as selecting, randomizing, and listing the diagnoses\u0026mdash;were clearly detailed in the study. The research was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u003c/p\u003e\n\u003cp\u003eAuthor details \u003c/p\u003e\n\u003cp\u003e1 PhD, Assist. Prof. Dr; Department of Nursing, Faculty of Health Sciences, Amasya University, 05100 Amasya, Turkey.2 PhD, Prof. Dr; 2 Department of Fundamentals of Nursing, Faculty of Nursing, Ege University, Izmir, Turkey, 35100 Izmir, Turkey. 3 \u003cstrong\u003e,\u003c/strong\u003e Nursing, Merzifon Kara Mustafa Paşa State Hospıtal, Amasya, Turkey. 35100 Amasya, Turkey. 4 Nursing, Amasya Fine Arts High School, Amasya, Turkey. 35100 Amasya, Turkey. 5 Nursing, Sabuncuoğlu Şerefeddin Traınıng And Research Hospıtal, Amasya, Turkey. 35100 Amasya, Turkey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... \u0026amp; Amodei, D. (2020). Language models are few-shot learners. \u003cem\u003earXiv preprint arXiv:2005.14165\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOpenAI. (2024). \u003cem\u003eGPT-4 technical report\u003c/em\u003e. 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Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. \u003cem\u003ePLOS Digital Health, 2\u003c/em\u003e(2), e0000198\u003c/li\u003e\n\u003cli\u003eBorji, A. (2023). A categorical archive of ChatGPT failures. \u003cem\u003earXiv preprint arXiv:2302.03494\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eGordijn, B., \u0026amp; Have, H. T. (2023). ChatGPT: Evolution or revolution in academic publishing? \u003cem\u003eMedicine, Health Care and Philosophy, 26\u003c/em\u003e(1), 1\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eCotton, D. R. E., Cotton, P. A., \u0026amp; Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. \u003cem\u003eInnovations in Education and Teaching International, 60\u003c/em\u003e(2), 176\u0026ndash;187.\u003c/li\u003e\n\u003cli\u003eKasneci, E., Sessler, K., K\u0026uuml;bler, R., Bannert, M., Dementieva, D., Fischer, F., \u0026amp; Kasneci, G. (2023). ChatGPT for good? 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(2016). \u003cem\u003eStatistics and data analysis: An introduction\u003c/em\u003e (2nd ed.). Wiley.\u003c/li\u003e\n\u003cli\u003eHair, J. F., Black, W. C., Babin, B. J., \u0026amp; Anderson, R. E. (2010). \u003cem\u003eMultivariate data analysis\u003c/em\u003e (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.\u003c/li\u003e\n\u003cli\u003eHerdman, T. H., \u0026amp; Kamitsuru, S. (Eds.). (2021). \u003cem\u003eNANDA International nursing diagnoses: Definitions and classification, 2021\u0026ndash;2023\u003c/em\u003e. Thieme Medical Publishers.\u003c/li\u003e\n\u003cli\u003eLohr, S. L. (2022). \u003cem\u003eSampling: Design and analysis\u003c/em\u003e (3rd ed.). Chapman and Hall/CRC.\u003c/li\u003e\n\u003cli\u003eSmith, T. J., \u0026amp; Johnson, K. M. (2022). Evaluating AI interfaces for reproducibility in medical research. \u003cem\u003eAI in Medicine, 126\u003c/em\u003e, 102153.\u003c/li\u003e\n\u003cli\u003eChen, Y., \u0026amp; Li, M. (2023). Evaluating AI-generated references: A comparison of real and fabricated citations. \u003cem\u003eAI \u0026amp; Society, 38\u003c/em\u003e(3), 921\u0026ndash;936.\u003c/li\u003e\n\u003cli\u003eiThenticate. (2024). \u003cem\u003ePlagiarism detection software\u003c/em\u003e. Retrieved from https://www.ithenticate.com\u003c/li\u003e\n\u003cli\u003eTurnitin. (2024). \u003cem\u003eSimilarity and AI detection tool\u003c/em\u003e. Retrieved from https://www.turnitin.com\u003c/li\u003e\n\u003cli\u003eZamanian, M., \u0026amp; Heydari, P. (2012). Readability of texts: State of the art. \u003cem\u003eProcedia - Social and Behavioral Sciences, 70\u003c/em\u003e, 1218\u0026ndash;1228.\u003c/li\u003e\n\u003cli\u003eDuBay, W. H. (2004). \u003cem\u003eThe principles of readability\u003c/em\u003e. Costa Mesa, CA: Impact Information.\u003c/li\u003e\n\u003cli\u003eKher, A., Johnson, S., \u0026amp; Griffith, R. (2017). Readability assessment of online patient education materials. \u003cem\u003eJournal of Community Hospital Internal Medicine Perspectives, 7\u003c/em\u003e(1), 34\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eCharnock, D., Shepperd, S., Needham, G., \u0026amp; Gann, R. (1999). DISCERN: An instrument for judging the quality of written consumer health information on treatment choices. \u003cem\u003eJournal of Epidemiology \u0026amp; Community Health, 53\u003c/em\u003e(2), 105\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eCerminara, G., De Rosa, M., \u0026amp; Palombi, L. (2021). Validating the DISCERN tool in the digital health context: A usability study. \u003cem\u003eJMIR Public Health and Surveillance, 7\u003c/em\u003e(4), e25990.\u003c/li\u003e\n\u003cli\u003eOzduran, E., Akkoc, I., B\u0026uuml;y\u0026uuml;k\u0026ccedil;oban, S., Erkin, Y., \u0026amp; Hancı, V. (2025). Readability, reliability and quality of responses generated by ChatGPT, Gemini, and Perplexity for the most frequently asked questions about pain. \u003cem\u003eMedicine, 104\u003c/em\u003e(11), e41780. https://doi.org/10.1097/MD.0000000000041780\u003c/li\u003e\n\u003cli\u003eKim, J., Lee, Y., \u0026amp; Kim, M. (2023). Readability of palliative care content generated by AI chatbots. \u003cem\u003ePalliative Medicine Reports, 4\u003c/em\u003e(1), 17\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eGul, A., Yalcın, G., \u0026amp; \u0026Ouml;zdemir, Y. (2023). The adequacy of chatbots in producing medical information: The case of subdural hematoma. \u003cem\u003eTurkish Neurosurgery Journal, 33\u003c/em\u003e(2), 89\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eHaver, J. S., Li, H., \u0026amp; Liu, Y. (2023). Understanding lung cancer through AI-generated content: A readability study. \u003cem\u003eJournal of Thoracic Oncology, 18\u003c/em\u003e(2), 145\u0026ndash;152.\u003c/li\u003e\n\u003cli\u003eWoodnutt, T., Lane, R., \u0026amp; Johnson, B. (2023). Evaluating goal specificity in AI-generated care plans. \u003cem\u003eJournal of Clinical Nursing, 32\u003c/em\u003e(7\u0026ndash;8), 1320\u0026ndash;1332.\u003c/li\u003e\n\u003cli\u003eLee, H., Park, S., \u0026amp; Choi, M. (2023). Analyzing ChatGPT and Gemini responses to hypertension education queries. \u003cem\u003eJournal of Medical Internet Research, 25\u003c/em\u003e, e45739.\u003c/li\u003e\n\u003cli\u003eAriyaratne, D., Abeyratne, A. I., \u0026amp; Samarasinghe, S. (2023). Hallucinated citations in AI-generated articles: A cross-comparison of sources. \u003cem\u003eJournal of Medical Informatics, 45\u003c/em\u003e(2), 112\u0026ndash;120.\u003c/li\u003e\n\u003cli\u003eGao, C., \u0026amp; Xue, X. (2023). Artificial intelligence hallucinations: Risks of misinformation in health communication. \u003cem\u003eHealth Informatics Journal, 29\u003c/em\u003e(3), 1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eSmith, M., \u0026amp; Lee, H. (2023). Public perception of ChatGPT in education: A sentiment analysis of Twitter content. \u003cem\u003eComputers \u0026amp; Education: Artificial Intelligence, 4\u003c/em\u003e, 100127.\u003c/li\u003e\n\u003cli\u003eJones, L., Brown, R., \u0026amp; Wang, Y. (2023). AI in healthcare: Patient empowerment or risk? \u003cem\u003eBMJ Innovations, 9\u003c/em\u003e(1), 1\u0026ndash;5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, chatbot, ChatGPT, nursing care, nursing education, nursing care plan","lastPublishedDoi":"10.21203/rs.3.rs-7237457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7237457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eResearch on ChatGPT-supported nursing care plan texts plays a critical role in making nursing education more innovative and accessible. These studies strengthen education by improving the readability, reliability, and quality of the texts.\u003c/p\u003e\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eThis study aims to evaluate the readability, reliability, and quality of nursing care plan texts generated by ChatGPT.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThe study sample consisted of 50 texts generated by ChatGPT based on selected nursing diagnoses from NANDA 2021\u0026ndash;2023. These texts were evaluated using a descriptive criteria form, the DISCERN tool, and readability indices including the Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index, and Flesch-Kincaid Grade Level (FKGL).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAccording to our findings, the readability level of the nursing care plans generated by ChatGPT was significantly higher than the recommended 6th-grade level (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). The mean DISCERN score was 45.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72, indicating a moderate level of reliability for all evaluated texts. Additionally, 97.5% of the texts also achieved moderate scores on the information quality subscale. A positive and statistically significant correlation was found between the number of verifiable references and both the reliability (r\u0026thinsp;=\u0026thinsp;0.408) and quality (r\u0026thinsp;=\u0026thinsp;0.379) scores of the texts (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eIt should be noted that these AI-based chatbot tools cannot replace comprehensive patient care plans. In AI applications, it is recommended that the readability of generated content be improved, reliable references be included, and all outputs be reviewed by a professional team.\u003c/p\u003e","manuscriptTitle":"Readability, Reliability, and Quality of Nursing Care Plan Texts Generated by Chatgpt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:13:21","doi":"10.21203/rs.3.rs-7237457/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-26T06:51:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T13:04:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T20:12:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T07:19:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53594361513349563090488426432099714469","date":"2025-09-03T14:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97527133605054949940589142182903300547","date":"2025-09-02T14:13:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220246861108146226387354582802381794346","date":"2025-08-24T09:38:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49080471274667416334895435142597414716","date":"2025-08-22T08:53:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T08:42:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-30T09:39:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T06:55:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T06:55:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-07-28T22:22:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36276c1b-0b53-4359-9dee-94fc25c6f81f","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:03:54+00:00","versionOfRecord":{"articleIdentity":"rs-7237457","link":"https://doi.org/10.1186/s12912-025-04171-w","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2025-11-27 15:58:21","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-09-01 09:13:21","video":"","vorDoi":"10.1186/s12912-025-04171-w","vorDoiUrl":"https://doi.org/10.1186/s12912-025-04171-w","workflowStages":[]},"version":"v1","identity":"rs-7237457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7237457","identity":"rs-7237457","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}