Objectives: This study examined the accessibility and utilization of sexual and reproductive health (SRH) services among ethnic minority populations in Vietnam’s mountainous regions, with a focus on their perceptions and challenges. Methods: A mixed-methods design was employed, combining a structured survey of 630 adults with semi-structured interviews and focus group discussions to capture both utilization patterns and contextual experiences. Results: Most participants were of Thai ethnicity (77%). Commune health stations and district hospitals were the most frequently used facilities, primarily for genital tract diseases, pregnancy, and contraceptive services. Over 80% reported satisfaction with services; however, major barriers included long waiting times, limited transportation, and geographical isolation. Respondents expressed a preference for district-level and private facilities due to perceived higher quality, while residents in remote areas often relied on village midwives for maternal care. Additional challenges included the absence of school health staff and the financial burden on teachers providing basic medical support. Conclusion: Findings highlight both service availability and persistent inequities. Addressing logistical, cultural, and systemic barriers through culturally sensitive policies and programs is essential to improving SRH equity for ethnic minority communities in Vietnam.

This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.

Purpose: This study evaluates the use of ChatGPT-4o in creating tailored continuing professional development (CPD) plans for radiography students, addressing the challenge of aligning CPD with Medical Radiation Practice Board of Australia (MRPBA) requirements. We hypothesized that ChatGPT-4o could support students in CPD planning while meeting regulatory standards. Methods: A descriptive, experimental design was used to generate 3 unique CPD plans using ChatGPT-4o, each tailored to hypothetical graduate radiographers in varied clinical settings. Each plan followed MRPBA guidelines, focusing on computed tomography specialization by the second year. Three MRPBA-registered academics assessed the plans using criteria of appropriateness, timeliness, relevance, reflection, and completeness from October 2024 to November 2024. Ratings underwent analysis using the Friedman test and intraclass correlation coefficient (ICC) to measure consistency among evaluators. Results: ChatGPT-4o generated CPD plans generally adhered to regulatory standards across scenarios. The Friedman test indicated no significant differences among raters (P=0.420, 0.761, and 0.807 for each scenario), suggesting consistent scores within scenarios. However, ICC values were low (–0.96, 0.41, and 0.058 for scenarios 1, 2, and 3), revealing variability among raters, particularly in timeliness and completeness criteria, suggesting limitations in the ChatGPT-4o’s ability to address individualized and context-specific needs. Conclusion ChatGPT-4o demonstrates the potential to ease the cognitive demands of CPD planning, offering structured support in CPD development. However, human oversight remains essential to ensure plans are contextually relevant and deeply reflective. Future research should focus on enhancing artificial intelligence’s personalization for CPD evaluation, highlighting ChatGPT-4o’s potential and limitations as a tool in professional education.

This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.

Purpose: This study evaluates the use of ChatGPT-4o in creating tailored continuing professional development (CPD) plans for radiography students, addressing the challenge of aligning CPD with Medical Radiation Practice Board of Australia (MRPBA) requirements. We hypothesized that ChatGPT-4o could support students in CPD planning while meeting regulatory standards. Methods: A descriptive, experimental design was used to generate 3 unique CPD plans using ChatGPT-4o, each tailored to hypothetical graduate radiographers in varied clinical settings. Each plan followed MRPBA guidelines, focusing on computed tomography specialization by the second year. Three MRPBA-registered academics assessed the plans using criteria of appropriateness, timeliness, relevance, reflection, and completeness from October 2024 to November 2024. Ratings underwent analysis using the Friedman test and intraclass correlation coefficient (ICC) to measure consistency among evaluators. Results: ChatGPT-4o generated CPD plans generally adhered to regulatory standards across scenarios. The Friedman test indicated no significant differences among raters (P=0.420, 0.761, and 0.807 for each scenario), suggesting consistent scores within scenarios. However, ICC values were low (–0.96, 0.41, and 0.058 for scenarios 1, 2, and 3), revealing variability among raters, particularly in timeliness and completeness criteria, suggesting limitations in the ChatGPT-4o’s ability to address individualized and context-specific needs. Conclusion ChatGPT-4o demonstrates the potential to ease the cognitive demands of CPD planning, offering structured support in CPD development. However, human oversight remains essential to ensure plans are contextually relevant and deeply reflective. Future research should focus on enhancing artificial intelligence’s personalization for CPD evaluation, highlighting ChatGPT-4o’s potential and limitations as a tool in professional education.

This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.

Purpose: This study evaluates the use of ChatGPT-4o in creating tailored continuing professional development (CPD) plans for radiography students, addressing the challenge of aligning CPD with Medical Radiation Practice Board of Australia (MRPBA) requirements. We hypothesized that ChatGPT-4o could support students in CPD planning while meeting regulatory standards. Methods: A descriptive, experimental design was used to generate 3 unique CPD plans using ChatGPT-4o, each tailored to hypothetical graduate radiographers in varied clinical settings. Each plan followed MRPBA guidelines, focusing on computed tomography specialization by the second year. Three MRPBA-registered academics assessed the plans using criteria of appropriateness, timeliness, relevance, reflection, and completeness from October 2024 to November 2024. Ratings underwent analysis using the Friedman test and intraclass correlation coefficient (ICC) to measure consistency among evaluators. Results: ChatGPT-4o generated CPD plans generally adhered to regulatory standards across scenarios. The Friedman test indicated no significant differences among raters (P=0.420, 0.761, and 0.807 for each scenario), suggesting consistent scores within scenarios. However, ICC values were low (–0.96, 0.41, and 0.058 for scenarios 1, 2, and 3), revealing variability among raters, particularly in timeliness and completeness criteria, suggesting limitations in the ChatGPT-4o’s ability to address individualized and context-specific needs. Conclusion ChatGPT-4o demonstrates the potential to ease the cognitive demands of CPD planning, offering structured support in CPD development. However, human oversight remains essential to ensure plans are contextually relevant and deeply reflective. Future research should focus on enhancing artificial intelligence’s personalization for CPD evaluation, highlighting ChatGPT-4o’s potential and limitations as a tool in professional education.

Backgrounds: Fecal occult blood testing (FOBT) is commonly used in colorectal cancer screening programs. Many studies have compared different FOBT methods, but the correlation between traditional red cell microscopy and FOBT remains unclear. Objectives: 1) To evaluate the rate of positive FOBT in patients with different disease groups; 2) To compare the sensitivity and specificity of red blood cells detection in fresh stool by microscopy technique and FOBT. Materials and methods: This was a cross-sectional study involving 120 patients from Hue University of Medicine and Pharmacy Hospital who requested a stool test from 4/2021 to 4/2022. Fresh stool samples were examined for the presence of red blood cells using traditional microscopy and FOBT technique. Results: The overall positivity rate of FOBT was 20%, and in the group of gastrointestinal diseases (n = 24), clinical anemia (n = 21), hepatobiliary diseases (n = 26) and other diseases (n = 49), it was 37.5%, 23.8%, 11.5% and 14.3%, respectively. In comparison with the FOBT technique, microscopic RBC detection had a sensitivity of 33.3% and a specificity of 100%. Conclusions: A high rate of fecal occult blood tests was observed in patients with gastrointestinal disorders. Microscopic erythrocyte detection has low sensitivity and many disadvantages compared to the rapid test. This rapid test should be widely used in clinical practice to aid in the diagnosis of gastrointestinal bleeding

Backgroud: The aim of this study is to update on antibiotic resistance of common pathogenical bacteria isolated in Hue University of Medicine and Pharmacy Hospital (Hue UMP Hospital). Methodology: Use of the agar disk diffusion method to test the susceptibility to antimicrobial agents of 3709 bacterial strains from infected patients hospitalized in Hue UMP Hospital in 2020 - 2022. Results: Among 3709 strains of pathogenical bacteria isolated, S.aureus was found with the rate of 29.9%, followed by E. coli (24.5%), Pseudomonas aeruginasa (17.8%), Enterococcus spp. (11.8%), Klebsiella spp (9.7%) and Acinetobacter spp (4.1%). The proportion of bacterial isolates has changed, but Staphylococcus aureus is still highest rate. S.aureus is resistant to many antibiotics, but MRSA strains have decreased significantly, from 73.3% in 2020 to 62.5% in 2022. Pseudomonas aeruginosa was resistant to some of the group A recommended antibiotics such as ceftazidime, piperacillin-tazobactam with the rate of 56.6% and 48.7%. The percentage of E. coli with ESBL strains (+) was at 28.2% - 30.3%. Enterococus spp strains are still sensitive to vancomycin (83.1% - 91.9%). The rate of Klebsiella ESBL (+) is only 6.9% to 8.2%. The strains of Acinetobacter spp were highly resistant to Piperacillin (100%) and Ceftriaxone (96.5%) but they are still sensitive to imipenems 70 - 71%, highly sensitive to Doxycillin (95.2%) and Cefotaxime (88.4%). Conclusion: Many bacterial strains are resistant to many commonly antibiotics. Providing timely, regular, and effective management of antibiotic resistance patterns for common pathogenic bacteria in hospitals, will help reduce the risk of bacterial resistance.

Background: Good sleep is important for health and overall quality of life. However, sleep disorders are currently widespread and have particularly serious consequences, especially for students. Objectives: Surveying sleep quality according to the Pittsburgh scale (PSQI) and find out some related factors according to traditional medicine of regular students at the University of Medicine and Pharmacy, Hue University. Methods: A cross-sectional descriptive study on 421 regular students from 10 majors of the Hue University of Medicine and Pharmacy. Results: The percentage of students having poor sleep quality according to the PSQI scale was 57%. According to traditional medicine, heart blood deficiency accounted for the highest proportion with 30.4%, next are the deficiency of heart and spleen (22.1%), kidney yin deficiency (20.8%), qi deficiency of heart and gallbladder (14.6%) and stomach disharmony has the lowest rate with 12.1%. The percentage of students who need treatment with traditional medicine was 52.9%, acupress massage and herbal medicine were the two methods with the highest demand for treatment. Conclusion: Assessing sleep quality according to the PSQI scale the percentage of students with poor sleep quality was 57%. Heart blood deficiency accounted for the highest proportion. The demand for treatment of insomnia by traditional medicine was relatively high.