Correct Answer Rate Research Articles

Artificial intelligence performance in clinical neurology queries: the ChatGPT model

ABSTRACT Introduction The use of artificial intelligence technology is progressively expanding and advancing in the health and biomedical literature. Since its launch, ChatGPT has rapidly gained popularity and become one of the fastest-growing artificial intelligence applications in history. This study evaluated the accuracy and comprehensiveness of ChatGPT-generated responses to medical queries in clinical neurology. Methods We directed 216 questions from different subspecialties to ChatGPT. The questions were classified into three categories: multiple-choice, descriptive, and binary (yes/no answers). Each question in all categories was subjectively rated as easy, medium, or hard according to its difficulty level. Questions that also tested for intuitive clinical thinking and reasoning ability were evaluated in a separate category. Results ChatGPT correctly answered 141 questions (65.3%). No significant difference was detected in the accuracy and comprehensiveness scale scores or correct answer rates in comparisons made according to the question style or difficulty level. However, a comparative analysis assessing question characteristics revealed significantly lower accuracy and comprehensiveness scale scores and correct answer rates for questions based on interpretations that required critical thinking (p = 0.007, 0.007, and 0.001, respectively). Conclusion ChatGPT had a moderate overall performance in clinical neurology and demonstrated inadequate performance in answering questions that required interpretation and critical thinking. It also displayed limited performance in specific subspecialties. It is essential to acknowledge the limitations of artificial intelligence and diligently verify medical information produced by such models using reliable sources.

Tsunami hazard perception and knowledge of alert: early findings in five municipalities along the French Mediterranean coastlines

Abstract. Along the French Mediterranean coastlines, most of the studies dealing with tsunamis have focused on hazards, evacuations, or effects of awareness actions, as opposed to hazard perception and knowledge of alert declared locally by the population. To bridge this gap, in this study, data collection yielded 750 responses coming from 150 people living and/or working in the tsunami evacuation zones of five municipalities (e.g., Bandol, Bastia, Cannes, Six-Fours-les-Plages, Sanary-sur-Mer). Early findings confirmed the tendency to underestimate tsunami hazard and a misunderstanding of the alert process. Interestingly, age or location of respondents explained differences between the five surveyed municipalities – more so than gender and residency status. Discrepancies are also observed when we compare the rate of correct answers for similar questions addressed in other areas in the NEAMTWS (North-eastern Atlantic, Mediterranean and connected seas Tsunami Warning System) region, thus confirming local effects. More surprisingly, the respondents who well perceive the tsunami hazard are not those who have a good knowledge of alerts, and the awareness actions do not really impact the tsunami knowledge we evaluated. Also, the results of this study could help local authorities to develop future tsunami awareness actions and to determine more suitable strategies to be applied in the short term at local scales.

Evaluation of Medium-Sized Language Models in German and English Language

Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.

Comparison of clinical dentistry students' knowledge and attitudes about obstructive sleep apnea syndrome before and after extra-curricular education

Background: Obstructive Sleep Apnea (OSA) is difficult to detect when dentists or dentistry students are not well-trained on this subject. Including this topic in the dental curriculum may help students with diagnosing patients who have OSA. Objectives: This cross-sectional study was conducted to compare the changes in the knowledge and attitudes of clinical dentistry students at Atatürk University, Turkey about OSA before and after education about this topic. Materials and methods: In this study, 227 participants were asked to reply OSAKA questionnaire consisting of 23 questions (18 knowledge questions and 5 attitude evaluation questions) before and after the lesson about OSA. Obtained data were analyzed with descriptive statistics, Chi-square, and Spearman analyses. The statistical significance level was set at α < 0.05. Results: The mean knowledge score was 4.91 before the lesson and 13.0 after the class. Overall, correct answer rates increased significantly (p = 0.010) as well. Conclusion: Participants' knowledge about OSA increased and their attitudes became more positive after the lesson. Pre-graduate training may increase dentistry students’ knowledge scores and improve their attitudes regarding OSA.

Glass Particle Contamination and Using the Filter Needle: A Cross-sectional Study

ABSTRACT: Purpose: To identify the levels of knowledge of nurses and physicians about the contamination of micro-sized glass particles in medicine content when opening glass ampoules containing parenteral medicines and the use of filter needles in reducing contamination. When the medicine ampoule is broken and opened, spill into the ampoule and contaminates the medicine. Medicines contaminated with glass particles pose a potential danger when they are administered parenterally to patients. Material and Methods: This study was planned as a descriptive cross-sectional study. The study was conducted with a total of 600 healthcare professionals, 300 physicians and 300 nurses, working in a university hospital between October and November 2020. The data were collected using the “Personal Information Form” and the “Information Form about Glass Particle Contamination and Filter Needle Use”. Results: The highest response to the question "medicine contaminated with glass particles can cause complications in the patient" was I do not know at 52.7%. The highest number of correct answers at the rate of 76% was given to the question "particle scattering to buffer/fingers at glass ampoule opening”. When nurses were asked "presence of micro-sized glass particles in the medicine drawn from the ampoule to the syringe" and "preventing from giving glass particles with the medicine to the patient", the rate of correct answers was higher with significant differences compared to physicians. Conclusion: Healthcare professionals had moderate knowledge of glass particle contamination and the use of filter needles. Variables such as profession, number of ampoules used in the clinic, and educational status affect the level of knowledge. Findings inform to stresses the need to raise awareness for reducing glass particle contamination. Keywords: Contamination, Filter needle, Glass particle, Healthcare professionals

Performance of ChatGPT and Bard in self-assessment questions for nephrology board renewal.

Large language models (LLMs) have impacted advances in artificial intelligence. While LLMs have demonstrated high performance in general medical examinations, their performance in specialized areas such as nephrology is unclear. This study aimed to evaluate ChatGPT and Bard in their potential nephrology applications. Ninety-nine questions from the Self-Assessment Questions for Nephrology Board Renewal from 2018 to 2022 were presented to two versions of ChatGPT (GPT-3.5 and GPT-4) and Bard. We calculated the correct answer rates for the five years, each year, and question categories and checked whether they exceeded the pass criterion. The correct answer rates were compared with those of the nephrology residents. The overall correct answer rates for GPT-3.5, GPT-4, and Bard were 31.3% (31/99), 54.5% (54/99), and 32.3% (32/99), respectively, thus GPT-4 significantly outperformed GPT-3.5 (p<0.01) and Bard (p<0.01). GPT-4 passed in three years, barely meeting the minimum threshold in two. GPT-4 demonstrated significantly higher performance in problem-solving, clinical, and non-image questions than GPT-3.5 and Bard. GPT-4's performance was between third- and fourth-year nephrology residents. GPT-4 outperformed GPT-3.5 and Bard and met the Nephrology Board renewal standards in specific years, albeit marginally. These results highlight LLMs' potential and limitations in nephrology. As LLMs advance, nephrologists should understand their performance for future applications.

The Effect of Web-based Sedation Management Education on the Knowledge and Practices of Intensive Care Nurses

Aim: The research was conducted to evaluate the effect of web-based education on the knowledge and practices of intensive care nurses about sedation management.&#x0D; Material and Method: In this study, which was carried out in a single group, pre-test-post-test design, the knowledge levels and practices of 62 intensive care nurses on sedation management were evaluated. The nurses were given web-based sedation management education, one month after the education, the knowledge levels and practices of the nurses participating in the study on sedation management in intensive care patients were re-evaluated, and the effectiveness of the education was evaluated by analyzing the data before and after the education.&#x0D; Results: After the web-based education, it was determined that there was a significant increase in the rate of correct answers to the statements about sedation management by nurses. It was determined that the rate of nurses applying nursing interventions to prevent agitation and reduce the sedation need of patients increased significantly after the education.&#x0D; Conclusion: It has been determined that web-based education is an approach that supports increasing the knowledge and practices of intensive care nurses on sedation management. It is recommended that web-based education programs be supported in order for intensive care nurses to perform effective agitation-sedation management.

2. ARTIFICIAL INTELLIGENCE IN MEDICINE: A COMPARATIVE STUDY OF CHATGPT'S LEARNING CAPABILITY IN RESOLVING MEDICAL SPECIALIZATION QUESTIONS

Abstract Introduction ChatGPT is a Natural Language Processing (NLP) model trained via machine learning algorithms that has proven capable of passing multiple selective medical tests. The aim of this study is to evaluate its “self-learning” capacity over a 90-day interval in solving specialized medical questions. Methods A comparative descriptive analysis of the problem-solving ability of ChatGPT - 3.5 (10/02/2023 - 15/02/2023) versus ChatGPT - 4 (11/05/2023 - 13/05/2023) was conducted on the competitive exams for the position of Specialist in Thoracic Surgery, as announced by the Andalusian Health Service in 2022. This competitive exam was chosen because of its multiple-choice structure and its division into a theoretical questionnaire and a practical questionnaire. Results Success rate: ChatGPT - 3.5 58.90% (86/146): theoretical questionnaire 63.2% (62/98) and practical questionnaire 50% (24/48) / ChatGPT - 4 65.7% (96/146): theoretical questionnaire 71.43% (70/98) and practical questionnaire 54.16% (26/48). Inferential analysis showed no significant differences (p&amp;gt;0.05) with regard to the rate of correct answers between both versions globally, nor with respect to the theoretical or practical questionnaire. Conclusion Despite the constant “self-learning” of this Artificial Intelligence model, its performance in solving specialized medical questions, which require critical reasoning, remains an ongoing developmental challenge.

Survey of Pharmacist-Led Medication Guidance for Patients with Inflammatory Bowel Disease in Japan.

In pharmacotherapy for inflammatory bowel disease (IBD), good medication adherence is necessary to control the condition. However, some patients show poor adherence. Pharmacists need to provide appropriate medication guidance to improve medication adherence. Community pharmacists often have to provide medication guidance in case of insufficient patient information because of varying affiliations. Therefore, to help improve medication adherence in patients with IBD and establish cooperation between community and hospital pharmacies, we investigated the awareness of IBD among pharmacists and the actual status of pharmacist-led medication guidance for patients with IBD. This study comprised a knowledge test for IBD and a survey of medication guidance for IBD in the form of questionnaires, which were administered to pharmacists using web forms. Community pharmacy affiliation (p < 0.01) and having no experience in medication guidance for IBD (p < 0.01) contributed to low scores in the IBD knowledge test. There was a difference in the correct answer rate for interactions or screening tests prior to medication administration between community and hospital pharmacists. Medication guidance consultations involving residual drug adjustment (p < 0.01), confirmation of symptoms (p < 0.01), prescription from other hospitals (p = 0.04), therapeutic effects (p = 0.04), and confirmation of medication adherence were more common among community pharmacists than among hospital pharmacists. Cooperation between community and hospital pharmacies was most commonly achieved through tracing reports or personal medication handbooks. Improving pharmacists' awareness of IBD and sharing information are important to facilitate cooperation between community and hospital pharmacists to improve medication adherence.

Action‐time‐controlled tabletop gamification improves physician‒nurse collaborative emergency room evacuation training

AbstractThis article is an example of Lessons from the Field highlighting the practical implementation of a novel time‐controlled mechanism in the gamification of emergency department evacuation training via tabletop exercise. Tabletop exercise is one of the most common drill types for disaster preparedness. It is easy to use, effective, and low in cost, but it has some shortcomings. For example, its lack of authenticity is often criticized. We add a time‐control mechanism to the tabletop exercise and use settings similar to real events in an attempt to increase the authenticity of the tabletop exercise and the learning effect. We completed a 3‐h tabletop gamification of emergency room evacuation, which included a time control mechanism. Medical staff in our emergency department were asked to join this tabletop training. We evaluated the effectiveness of the time‐control tabletop exercise through the results of the pretest and posttests and feedback from the participants. A total of 97 emergency medical staff from the Emergency Department of Cheng Kung University Hospital, including 64 nurses, 29 emergency doctors and four nurse practitioners, participated in this tabletop exercise. After experiencing the time‐control tabletop exercise, the participants significantly improved their approach to mass casualty incidents, their method for evacuating patients, and their triage skills. The correct answer rate for the pretest and post‐test rose from 68.75% to 94.33% with statistically significant. The feedback from the participants also showed that the time‐control tabletop exercise was interesting and could increase learning motivation. The time‐control mechanism had a positive impact on the learning effect. It increased the reality of tabletop exercises, promoted participants' learning motivation, and improved their performance on the test.

Investigation of stratum corneum cell morphology and content using novel machine-learning image analysis.

The morphology and content of stratum corneum (SC) cells provide information on the physiological condition of the skin. Although the morphological and biochemical properties of the SC are known, no method is available to fully access and interpret this information. This study aimed to develop a method to comprehensively decode the physiological information of the skin, based on the SC. Therefore, we established a novel image analysis technique based on artificial intelligence (AI) and multivariate analysis to predict skin conditions. SC samples were collected from participants, imaged, and annotated. Nine biomarkers were measured in the samples using enzyme-linked immunosorbent assay. The data were then used to teach machine-learning models to recognize individual SC cell regions and estimate the levels of the nine biomarkers from the images. Skin physiological indicators (e.g., skin barrier function, facial analysis, and questionnaires) were measured or obtained from the participants. Multivariate analysis, including biomarker levels ​​and structural parameters of the SC as variables, was used to estimate these physiological indicators. We established two machine-learning models. The accuracy of recognition was assessed according to the average intersection over union (0.613), precision (0.953), recall (0.640), and F-value (0.766). The predicted biomarker levels significantly correlated with the measured levels. Skin physiological indicators and questionnaire answers were predicted with strong correlations and correct answer rates. Various physiological skin conditions can be predicted from images of the SC using AI models and multivariate analysis. Our method is expected to be useful for dermatological treatment optimization.

Better characterizing sleep beliefs for personalized sleep health promotion: the French sleep beliefs scale validation study.

The Sleep Beliefs Scale (SBS) is a well-known tool to design and monitor personalized sleep health promotion at an individual and population level. The lack of an established French version limits the development of effective interventions targeting these populations. Thus, the aim of this study was to validate the French version of the SBS in a representative sample of the general population. Quota sampling was used to recruit 1,004 participants (18-65 years, mean age: 43 years, 54% of female) who underwent an online survey to complete the SBS, and to assess sleep schedules, sleep quality and disorders, and mental health. Cronbach's α coefficient, confirmatory factor analysis, item-internal consistency (IIC), and item discriminant validity (IDV) of the SBS were computed to assess internal validity while bivariate associations with sleep schedules, sleep quality and disorders, and mental health were used to assess external convergent and discriminant validity. The mean score on the SBS was 12.3 ± 4.9. Item 19 ("Quiet & Dark") showed the highest rate of correct answers (n = 801, 79.8%), while item 20 ("Recovering sleep") showed the lowest rate of correct answers (n = 246, 24.5%). Overall, the SBS showed satisfactory internal consistency (α = 0.87) and confirmed the three-factor structure proposed by the original study. All items were found consistent (IIC > 0.4) and discriminant (IIC > IDV) except for item 20 ("recovering lost sleep by sleeping for a long time"). Females, older participants, and subjects with short time-in-bed, poor sleep quality, insomnia, and circadian rhythm disorder had higher SBS scores while participants with depressive symptoms had lower SBS scores. We successfully translated and validated the French version of the SBS in a representative sample, making it a reliable instrument for researchers and clinicians to assess and target sleep beliefs. Correct answers vary from 25 to 80% which underlines the importance of continuing sleep health promotion campaigns by targeting poorly understood behaviors. Our findings also shed light on the fickleness of beliefs that are prone to vary within individuals across time, in step with societal changes. Several associated factors were identified, thus contributing to our understanding of sleep beliefs and offering insights for personalized approaches to enhance sleep health and overall well-being.

How does artificial intelligence master urological board examinations? A comparative analysis of different Large Language Models' accuracy and reliability in the 2022 In-Service Assessment of the European Board of Urology.

This study is a comparative analysis of three Large Language Models (LLMs) evaluating their rate of correct answers (RoCA) and the reliability of generated answers on a set of urological knowledge-based questions spanning different levels of complexity. ChatGPT-3.5, ChatGPT-4, and Bing AI underwent two testing rounds, with a 48-h gap in between, using the 100 multiple-choice questions from the 2022 European Board of Urology (EBU) In-Service Assessment (ISA). For conflicting responses, an additional consensus round was conducted to establish conclusive answers. RoCAwas compared across various question complexities. Ten weeks after the consensus round, a subsequent testing round was conducted to assess potential knowledge gain and improvement in RoCA, respectively. Over three testing rounds, ChatGPT-3.5 achieved RoCa scores of 58%, 62%, and 59%. In contrast, ChatGPT-4 achieved RoCA scores of 63%, 77%, and 77%, while Bing AI yielded scores of 81%, 73%, and 77%, respectively. Agreement rates between rounds 1 and 2 were 84% (κ = 0.67, p < 0.001) for ChatGPT-3.5, 74% (κ = 0.40, p < 0.001) for ChatGPT-4, and 76% (κ = 0.33, p < 0.001) for BING AI. In the consensus round, ChatGPT-4 and Bing AI significantly outperformed ChatGPT-3.5 (77% and 77% vs. 59%, bothp = 0.010). All LLMs demonstrated decreasing RoCA scores with increasing question complexity (p < 0.001).In the fourth round, no significant improvement in RoCA was observed across all three LLMs. The performance of the tested LLMs in addressing urological specialist inquiries warrants further refinement. Moreover, the deficiency in response reliability contributes to existing challenges related to their current utility for educational purposes.

Analyzing knowledge, attitudes, and practices towards COVID-19: The second cross-sectional survey in the Cabo Verdean population

ContextThe emergence and increase of cases of the new coronavirus in Cabo Verde led to the implementation of several infection control measures. However, the success of these measures depends largely on the population's knowledge about the disease and their commitment to fighting it. This study aimed to analyze the knowledge, attitudes, perceptions, and practices of the resident Cabo Verdean population regarding COVID-19. MethodologyA cross-sectional study was carried out among 2754 participants aged 12 and above. The data collection instrument, a questionnaire, was applied in the field by trained surveyors. Descriptive statistics, chi-square tests, Spearman correlation, and simple and multiple regression analyses were performed to assess the sample's determinants of knowledge, attitudes, and practices towards COVID-19. ResultsOf the 2754 study participants, 1331 (48.3%) were male. The mean age of the respondents was 28.5 years (SD = 14.1). The overall knowledge rate was 79%. Logistic regression analysis showed that knowledge was significantly associated with secondary education or higher (p < 0.001), marital status married or de facto partnership (p < 0.001), and residence in the urban area of São Vicente (p = 0.025). The rate of correct answers concerning attitudes was 75.7%, with the majority of respondents agreeing with the measures implemented by the authorities in managing the pandemic (n = 2272; 82.9%). The results show considerable hesitation concerning the vaccine, which is significantly associated with the age group 18 to 30 (p < 0.001). Practices obtained the lowest scores, and physical distancing was one of the preventive measures least complied with. Binary logistic regression revealed a significant association between sex, age, education, and marital status with practices. Knowledge (rho = 0.1436; p < 0.001) and attitudes (rho = 0.1068; p < 0.001) were significantly associated with practices. ConclusionFinancial difficulties seem to hinder compliance with infection control measures. Public health policies may have greater adherence if accompanied by social and economic support strategies. Studies investigating the behavior of infected persons during the infection period may provide helpful information in understanding the mechanisms underlying the virus transmission in the country.

A Proposal of Code Writing Problem for C Programming Learning Assistant System

C programming has been a fundamental subject for a lot of university students studying programming lan guages, algorithms, and computer architecture. To enhance C programming education in schools, we have developed the C Programming Learning Assistant System (CPLAS) which provides a variety of programming assignments to cover different learning stages. The programming assignments offered by CPLAS allow the students to practice writing partial source code to learn grammar, code reading, and code debugging skills. However, the current assignments offered by CPLAS do not cover students to practice writing the whole C source code from scratch. Therefore, in this paper, we propose the Code Writing Problem (CWP) for students to write the C source code from scratch. In a CWP instance, a problem statement, an input list, and an expected output are given to the students. The students need to write the C source code from scratch by referring to the given information. The answer codes are marked through 1) compiling test, 2) execution test, and 3) output test. To avoid cheating by students, we use the random input generation method for each data type. To evaluate the proposal, we generated and assigned 11 CWP instances to the students. From their solution results, 10 out of 11 CWP instances achieved over 78% correct answer rate, and the feedback from the students confirmed the validity of the proposal.

Can ChatGPT help patients understand their andrological diseases?

We aimed to assess the reliability of Chat Generative Pre-training Transformer (ChatGPT)'s andrology information and its suitability for informing patients and medical students accurately about andrology topics. We presented a series of systematically organized frequently asked questions on andrology topics and sentences containing strong recommendations from the European Association of Urology (EAU) Guideline to ChatGPT-3.5 and 4.0 as questions. These questions encompassed Male Hypogonadism, Erectile Dysfunction and Sexual Desire Disorder, Disorders of Ejaculation, Penile Curvature and Penile Size Abnormalities, Priapism, and Male Infertility. Two expert urologists independently evaluated and assigned scores ranging from 1 to 4 to each response based on its accuracy, with the following ratings: (1) Completely true, (2) Accurate but insufficient, (3) A mixture of accurate and misleading information, and (4) Completely false. A total of 120 questions were included in the study. Among these questions, 50.0% received a grade of 1 (completely correct) (55.4% for 4.0 version). The combined rate of correct answers (grades 1 and 2) was 85.2% for frequently asked questions (88.8% for 4.0 version) and 81.5% for questions obtained from the guideline. The rate of completely incorrect answers (grade 4) was 1.8% for frequently asked questions (0% for 4.0 version) and 5.2% for questions based on strong recommendations. The response rate of version 4.0 to questions created from sentences containing strong recommendations from the EAU guideline was the same as version 3.5. ChatGPT provided satisfactory answers to the questions asked, although some responses lacked completeness. It may be beneficial to utilize ChatGPT under the guidance of a urologist to enhance patients' comprehension of their andrology issues.

Verification of the Accuracy of Cervical Spinal Cord Injury Prognosis Prediction Using Clinical Data-Based Artificial Neural Networks.

In patients with cervical spinal cord injury (SCI), we need to make accurate prognostic predictions in the acute phase for more effective rehabilitation. We hypothesized that a multivariate prognosis would be useful for patients with cervical SCI. We made two predictive models using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs). We adopted MLR as a conventional predictive model. Both models were created using the same 20 clinical parameters of the acute phase data at the time of admission. The prediction results were classified by the ASIA Impairment Scale. The training data consisted of 60 cases, and prognosis prediction was performed for 20 future cases (test cohort). All patients were treated in the Spinal Injuries Center (SIC) in Fukuoka, Japan. A total of 16 out of 20 cases were predictable. The correct answer rate of MLR was 31.3%, while the rate of ANNs was 75.0% (number of correct answers: 12). We were able to predict the prognosis of patients with cervical SCI from acute clinical data using ANNs. Performing effective rehabilitation based on this prediction will improve the patient's quality of life after discharge. Although there is room for improvement, ANNs are useful as a prognostic tool for patients with cervical SCI.

Knowledge of energy-based devices among surgical residents: a cross-sectional study.

This study evaluates the knowledge of surgical residents in a tertiary hospital regarding the operational principles, potential complications, and mechanisms of energy devices used in surgery. A 16-question test was developed using educational materials in the "Fundamental Use of Surgical Energy (FUSE)" program, prepared by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) for educational purposes, and from literature on energy devices. The test was shared online with surgical branch residents via Google Forms, and they were asked to complete it. Participants were given one week to complete the test and return the results online. A total of 108 residents voluntarily participated in the study. The average number of correct answers was 6.5 out of 16. There was no significant difference between the correct answer rates for surgical branches. However, an increase in surgical seniority was associated with a higher number of correct answers (P < 0.001). Theoretical education about energy devices in surgical training needs to be improved. Surgical residents reported insufficient knowledge regarding the devices' operational principles and complication mechanisms.

Anti-wrinkle properties of rosehip oil, aloe vera, green tea extract, and frankincense essential oil: Brief review and a survey

The knowledge and opinions of first-year pharmacy students were evaluated after conducting a survey. The survey included 5 knowledge-based and 5 opinion-based questions, focusing on four herbal remedies. The data collected from 28 participants were analyzed for correctness of answers and agreement or disagreement to opinion lead statements. The knowledge-based questions showed an average 74.5% correct answer rate, with the question on Rosehip Oil receiving the highest (about 93%) followed by the Green Tea Extract at 89.3%. Opinion-based questions revealed a positive attitude towards herbal remedies, with over 82% strongly agreeing or somewhat agreeing with their potential benefits. Over 92% of the respondents believed Green Tea Extract and Frankincense Essential Oil possess skin-protecting qualities. Participants had positive attitude towards herbal remedies for skincare. Knowledge-based questions had a satisfactory correct answer rate, and participants largely agreed (82.7%) on the potential of these herbal remedies to enhance skin health and reduce signs of aging. Disagreement was minimal, ranging from 0% to 17.2%, indicating a widespread inclination towards positive agreement. The survey results provide a valuable insight into the potential of herbal remedies in skincare. The knowledge level was satisfactory at 74.5%. Opinions towards Rosehip Oil, Aloe Vera, Green Tea Extract, and Frankincense Essential Oil were largely positive among the surveyed pharmacy students. Limitations of the survey include the small sample size and lack of diversity in the survey participants. Future research should address these limitations and explore additional factors influencing knowledge and opinions regarding herbal remedies for skincare.

Evaluation of non-healthcare professional women’s knowledge regarding ionizing radiation exposure hazards during pregnancy

BackgroundPregnant women undergoing radiation examinations should be given proper care, which requires a comprehensive understanding of the negative effects of ionizing radiation. PurposeThis study aimed to assess the knowledge of non-healthcare professional women regarding the potential hazards associated with ionizing radiation exposure during pregnancy. MethodsThis questionnaire-based cross-sectional study involved 290 women from various occupational groups, such as university academic staff, university students, hospital administrative staff, and patients aged between 20 and 50 years. A survey was conducted in the Kingdom of Saudi Arabia. Descriptive statistical techniques, including mean, standard deviation, and independent T-test were utilized to assess differences in knowledge levels among participants, with a significance level set at p ≤ 0.05. ResultsMedical staff exhibited a correct answer percentage of 40.8%, while non-medical staff scored 30.3%. Similarly, medical students achieved a 34.8% correct answer rate, whereas non-medical students scored 19.17%. These findings underscore a statistically significant difference in radiation awareness between medical and non-medical participants (p < 0.001).Doctorate holders exhibited a correct answer percentage of 43.8%, while master's degree holders scored 38%. In contrast, 6th-7th level students scored 22.6%, while 8th-9th level students achieved a 31.6% correct answer rate. These findings highlight a statistically significant difference in radiation awareness between faculty staff members and students based on their academic credentials (p < 0.001).Interestingly, the study found no significant difference in radiation awareness levels between hospital administrative staff and patients (p = 0.315). However, participants in hospital administration and patient disciplines displayed limited awareness of ionizing radiation and its hazards. ConclusionsThere is insufficient knowledge among women concerning the potential hazards of ionizing radiation exposure associated with radiological examinations during pregnancy. The introduction of educational initiatives focusing on radiation risk and protection during early stages of education is imperative.