bims-librar Biomed News
on Biomedical librarianship
Issue of 2025–08–24
thirteen papers selected by
Thomas Krichel, Open Library Society
  1. Single-Position Lumbar Fusion Online Videos for Patient Education: Understandability, Reliability, and Quality.
  2. Comparative analysis of large language models in providing patient information about keratoconus and contact lenses.
  3. Evaluating the Quality of Cardiovascular Disease Information From AI Chatbots: A Comparative Study.
  4. Demonstrating library value: the development of a customizable Library Value Planner.
  5. TikTok and Orthopaedic Education: Engaging the Next Generation of Patients.
  6. Enhancing the Readability of Online Pediatric Cataract Education Materials: A Comparative Study of Large Language Models.
  7. Evaluation of Google and ChatGPT responses to common patient questions about scoliosis.
  8. "I only knew how to search Google": students' reflections on a four-year information literacy curriculum.
  9. Comparing Large Language Models as Health Literacy Tools: Evaluating and Simplifying Texts on gender-Affirming Surgery.
  10. Comparative Assessment of Large Language Model Outputs and NHS Patient Information in Oral Medicine.
  11. Health Information-Seeking Behavior of Menopausal Women: An Online Survey of Iranian Menopausal Women.
  12. 'Living Well With a PICC at Home': Co-Design and Evaluation of a Peripherally Inserted Central Catheter (PICC) Booklet.
  13. Access to healthy ınformatıon: the ınteractıon of medıa lıteracy and health lıteracy.
  1. J Am Acad Orthop Surg Glob Res Rev. 2025 Aug 01. 9(8):
    Single-Position Lumbar Fusion Online Videos for Patient Education: Understandability, Reliability, and Quality.
    Chimobi Emukah, Jerod McCarrell, Tyler K Williamson, Victor H Martinez, Yusuf Mufti, Peter G Passias.
       INTRODUCTION: Patients undergoing surgical interventions, particularly spine surgery, often rely on online videos as a first source for medical information. In this study, we sought to investigate the understandability, reliability, and quality of online patient educational videos focused on single-position lumbar fusion.
    METHODS: The YouTube platform was searched using five search terms: prone transpsoas lumbar fusion, prone lateral lumbar fusion, single position lumbar fusion, prone single position lumbar fusion, and lateral single position lumbar fusion. The relevance-based ranking search option was used with each of the search terms and was investigated for videos relevant that met the inclusion criteria. Videos from academic institutions, commercial entities, individual physician, and physician groups were included for final evaluation in this study. The DISCERN instrument, patient education material assessment tool (PEMAT), the Global Quality Scale, and Journal of the American Medical Association Benchmark were used to evaluate the reliability, quality, and understandability of the videos.
    RESULTS: A total of 500 videos were initially identified, and after applying inclusion criteria, 13 videos were selected for evaluation. The videos exhibited an overall average score in each category: DISCERN (41.8 ± 10), PEMAT understandability (65% ± 12%), PEMAT actionability (28% ± 15%), Global Quality Scale (2.9 ± 0.4), and Journal of the American Medical Association Benchmark (2.9 ± 0.7). Notably, there was no discernible correlation between video popularity and the assigned scores. There was no correlation between video popularity and scores.
    DISCUSSION: The findings of this study demonstrate that patients who seek to access information about single-position lumbar fusion by using the YouTube platform will be presented with a moderate overall quality of educational content on this procedure. As single-position lumbar fusion becomes a more prevalent choice for patients, we recommend enhancing patient education on this spine surgery approach by encouraging academic institutions to produce standardized and dependable video educational materials. This would greatly benefit patients looking to better understand this procedure and make informed decisions.
    DOI:  https://doi.org/e24.00275
  2. Int Ophthalmol. 2025 Aug 18. 45(1): 340
    Comparative analysis of large language models in providing patient information about keratoconus and contact lenses.
    Yavuz Kemal Aribas, Atike Burcin Tefon Aribas.
       OBJECTIVE: To evaluate the accuracy, completeness, informational quality, and readability of responses generated by large language models (LLMs)-ChatGPT (OpenAI, USA), Gemini (Google, USA), and Copilot (Microsoft, USA)-to patient questions concerning keratoconus and contact lens use.
    METHODS: In this cross-sectional study, 32 questions across eight domains were posed to the free versions of each model. Two independent ophthalmologists rated accuracy (6-point Likert scale) and completeness (3-point Likert scale). Information quality was assessed using the DISCERN instrument, and readability was evaluated with the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). Inter-rater agreement was measured with Cohen's Kappa.
    RESULTS: Inter-rater reliability showed at least fair agreement for all LLMs. (min κ = 0.365) ChatGPT achieved significantly higher accuracy than Gemini (p < 0.001) and Copilot (p = 0.010), and higher completeness than Gemini (p = 0.001) but was similar to Copilot (p = 0.101). DISCERN scores were highest for ChatGPT (64), followed by Copilot (61) and Gemini (55). All models produced difficult-to-read content (FRES: Gemini 49.7, Copilot 45.4, ChatGPT 40.7), with FKGL values at late high school level.
    CONCLUSION: All evaluated large language models were capable of providing generally accurate and thorough information regarding keratoconus and contact lens use. Nevertheless, limitations in readability across models highlight the importance of clinician oversight to ensure that patient education remains clear, accessible, and appropriately tailored to individual needs.
    Keywords:  Artificial intelligence; ChatGPT; Contact lens; Health information quality; Keratoconus; Large language models (LLMs)
    DOI:  https://doi.org/10.1007/s10792-025-03711-2
  3. Cureus. 2025 Jul;17(7): e88085
    Evaluating the Quality of Cardiovascular Disease Information From AI Chatbots: A Comparative Study.
    Joshua Singavarapu, Amber Khemlani, Menachem Jacobs, Eli Berglas, Jason Lazar, Abdo Kabarriti.
      Artificial intelligence (AI) is increasingly being utilized as an informational resource, with chatbots attracting users for their ability to generate instantaneous responses. This study evaluates the understandability, actionability, readability, quality, and misinformation in medical information provided by four prominent chatbots - Bard, ChatGPT 3.5, Claude 2.0, and Perplexity - on three prevalent cardiovascular diseases (CVDs): myocardial infarctions, heart failure, and arrhythmias. These chatbots were used because of their popularity and high usage rates among chatbots. Using Google Trends, the top five U.S. search queries related to heart attack, arrhythmia, and heart failure from September 29, 2018, to September 29, 2023, were identified. The top five queries were chosen in relation to these topics because they accounted for over 80% of the public's searches related to these topics. The chatbot responses were blinded and analyzed by two evaluators using DISCERN for quality, Patient Education Materials Assessment Tool (PEMAT) for understandability and actionability, and Flesch-Kincaid scores for readability. Statistical tests included the Kruskal-Wallis test for DISCERN, the chi-square test for PEMAT, and one-way ANOVA for Flesch-Kincaid scores. Bard generated responses with a statistically lower Flesch-Kincaid reading score than the other chatbots. Bard and ChatGPT 3.5 provided more actionable responses. Among the CVD topics, "heart attack" yielded lower-grade-level responses and more actionable information compared to "arrhythmia" and "heart failure." This study is among the first to assess AI credibility in disseminating cardiovascular information. It highlights how acute pathologic events may prompt more actionable and accessible chatbot responses. As AI continues to evolve, collaboration among healthcare professionals, researchers, and developers is crucial to ensuring the safe and effective use of AI in patient education and public health.
    Keywords:  arrhythmia; artificial intelligence; cardiology; chatbot; chatgpt; claude; gemini; heart failure; myocardial infarction; perplexity
    DOI:  https://doi.org/10.7759/cureus.88085
  4. J Can Health Libr Assoc. 2025 Aug;46(2): 11-20
    Demonstrating library value: the development of a customizable Library Value Planner.
    Mark Mueller, Nicole Askin, Jeanna Hough, Brooke Ballantyne Scott, Joan Bartlett.
       Background: Library professionals in the health sciences sector need to strategically plan and map out library services. Each library and their parent organization have unique needs and service offerings.
    Objective: To develop an adaptable Library Value Planner (LVP) tool based on the Levels of Library Service benchmarking document developed by the Health Science Information Consortium (HSIC) that can be used for (i) strategic and operational planning and (ii) mapping out needs for implementing new library services in individual contexts.
    Methods: This project involved: (i) searching the literature; (ii) analyzing current trends and best practices in Canadian health libraries; (iii) updating and renaming of the Levels of Library Service document; (iv) drafting and disseminating a French and English survey; (v) leading French and English focus groups; (vi) analyzing the feedback received from the surveys and focus groups, and (vii) revising the tool based on this feedback.
    Results: The results from the surveys and the focus groups showed that participants were satisfied with the versatile nature of the LVP. Some respondents expressed concerns about the formatting of the LVP and others were not sure how and when the LVP ought to be used. This feedback highlighted the need to develop and disseminate education for library professionals about the tool.
    Conclusion: The CHLA/ABSC Standards Standing Committee developed a flexible and robust tool that, when paired with education, can be used to advocate and demonstrate the value of library services in the health sciences.
    DOI:  https://doi.org/10.29173/jchla29825
  5. J Am Acad Orthop Surg Glob Res Rev. 2025 Aug 01. 9(8):
    TikTok and Orthopaedic Education: Engaging the Next Generation of Patients.
    Camila Vicioso, Charu Jain, Uma Balachandran, Ryan Smolarsky, Laurel Wong, Luca Valdivia, Julian Javier, Auston Locke, James Hong, Sheena C Ranade.
       INTRODUCTION: Social media is increasingly pivotal in healthcare communication, with TikTok emerging as a leading platform because of its visually engaging, short-format videos. With nearly 2 billion users spending over 50 minutes daily on the app, TikTok offers a novel medium for disseminating orthopaedic information. Pediatric fractures-employed here as a representative model-are particularly relevant given the high social media use among youth and caregivers. This study investigates TikTok's role as an educational tool for orthopaedic surgeons and examines public engagement.
    METHODS: A cross-sectional study was conducted analyzing TikTok videos related to pediatric fractures. A newly created TikTok account was used to identify the top popular videos for each fracture type, excluding non-English, off-topic, private, or duplicate content. Engagement metrics, including views, likes, shares, comments, and bookmarks, were recorded. Video understandability was assessed using the Patient Education Materials Assessment Tool for Audiovisual Materials. Video reliability was assessed using the modified DISCERN scale. Videos were categorized by creator identity (physician, allied healthcare provider, patient, parent, and other).
    RESULTS: A total of 190 TikTok videos accumulated more than 25.7 million views and 1.8 million likes. Although nonexpert sources (parents and patients) contributed 87.9% of the content, healthcare professional-generated videos comprised 12.1% and demonstrated markedly higher engagement and quality scores. In particular, physician-produced content achieved the highest median views and shares (P < 0.01).
    DISCUSSION: The predominance of nonexpert content underscores a missed opportunity to leverage TikTok for disseminating reliable, evidence-based orthopaedic education. Expert-driven videos not only offer superior clarity, reliability, and actionable guidance but also align with the public's preference, suggesting broad applicability across orthopaedic subspecialties.
    CONCLUSION: TikTok represents a promising platform for enhancing orthopaedic education. Increasing healthcare professional engagement may improve the delivery of accurate, evidence-based content, ultimately advancing musculoskeletal health literacy and patient outcomes.
    DOI:  https://doi.org/e25.00152
  6. Transl Vis Sci Technol. 2025 Aug 01. 14(8): 19
    Enhancing the Readability of Online Pediatric Cataract Education Materials: A Comparative Study of Large Language Models.
    Xinyi Qiu, Chaokun Luo, Qingruo Zhang, Ka Yi Chung, Weirong Chen, Hui Chen.
       Purpose: The purpose of this study was to assess large language models (LLMs) for enhancing the readability of online patient education materials (PEMs) on pediatric cataracts through multilingual adaptation, content retrieval, and prompt engineering.
    Methods: This study included 103 PEMs presented in different languages and retrieved from diverse resources. Three LLMs (ChatGPT-4o, Gemini 2.0, and DeepSeek-R1) were used for content improvement. Readability was assessed for both the original and converted PEMs with multiple formulas. Different prompt engineering strategies for LLMs were also tested in this study.
    Results: The PEMs directly generated by LLMs exceeded a 10th grade reading level. Compared to a traditional Google search, LLMs' web browsing feature provided online PEMs with better characteristics and a higher reading level. Original PEMs from Google showed significantly improved readability after LLM conversion, with DeepSeek-R1 achieving the greatest reduction in reading level from 10.59 ± 2.20 to 7.01 ± 0.91 (P < 0.001). Prompt engineering also showed statistically significant results in their effects on LLM conversion, and Zero-shot-Cot (APE) successfully achieving target readability below the sixth grade reading level. Besides, the LLMs' simplified Chinese conversion, as well as the LLMs conversion of other original Chinese PEMs, both showed that they meet the recommended standards for reading levels in multiple dimensions.
    Conclusions: LLMs can significantly enhance the readability of multilingual online PEMs on pediatric cataract. Combining it with web browsing and prompt engineering can further optimize outcomes and advance patient education.
    Translational Relevance: This study links LLMs with patient education and demonstrates their potential to significantly improve the readability of online PEMs.
    DOI:  https://doi.org/10.1167/tvst.14.8.19
  7. Spine Deform. 2025 Aug 17.
    Evaluation of Google and ChatGPT responses to common patient questions about scoliosis.
    Sezgin Bahadır Tekin, Kamil Ince, Bedriye Gizem Tekin, Erkan Servet, Bahri Bozgeyik.
       OBJECTIVE: Scoliosis is primarily seen during adolescence and often causes significant concern among patients and their families when the deformity becomes noticeable. With technological advancements, patients frequently search the Internet for information regarding their disease's diagnosis, treatment, prognosis, and potential complications. This study aims to assess the quality of Google and ChatGPT responses to questions about scoliosis.
    METHODS: A search was conducted using Google with the keyword "scoliosis." The first ten questions listed under the "People Also Ask" (FAQs) section were recorded. Responses to these questions from ChatGPT and Google were evaluated using a four-level rating system: "Excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," and "unsatisfactory requiring substantial clarification." Additionally, the sources of the responses were categorized as academic, commercial, medical practice, governmental, or social media.
    RESULTS: ChatGPT provided "excellent responses requiring no explanation" for 9 out of 10 questions (90%). In contrast, none of Google's responses were categorized as excellent; 50% were unsatisfactory, requiring substantial clarification; 40% were satisfactory, requiring moderate clarification, and 10% were satisfactory, requiring minimal clarification. ChatGPT sourced 60% of its responses from academic resources and 40% from medical practice websites. Conversely, Google did not use scholarly sources, with 50% of reactions derived from commercial websites, 30% from medical practice sources, and 20% from social media. When the agreement between the 4 raters, regardless of AI, was examined using Fleiss Multirater Kappa in the reliability analysis, a statistically significant (p < 0.001) moderate agreement (κ = 0.48) was found.
    CONCLUSION: ChatGPT outperformed Google by providing more accurate, well-referenced responses and utilizing more credible academic sources. This suggests its potential as a more reliable tool for obtaining health-related information.
    Keywords:  Artificial Intelligence; ChatGPT; Google; Scoliosis
    DOI:  https://doi.org/10.1007/s43390-025-01169-x
  8. J Can Health Libr Assoc. 2025 Aug;46(2): 21-32
    "I only knew how to search Google": students' reflections on a four-year information literacy curriculum.
    Stephanie Sanger, Denise A Smith.
       Introduction: For undergraduate general health sciences education, there is insufficient high-quality evidence that speaks to the benefits and challenges of an integrated and scaffolded information literacy (IL) curriculum when compared to more traditional modes of delivery. Calls for scaffolded and curriculum-integrated programs are on the rise.
    Objectives: This study aims to understand students' perceived confidence and skill levels when engaging with or searching for health information, after four years of IL training.
    Methods: A mixed-methods survey was used to learn whether or how students' confidence in their IL skills was impacted by an IL curriculum integrated into the Bachelor of Health Sciences (BHSc) program at McMaster University. Quantitative survey questions were analysed with descriptive statistics. Qualitative survey questions underwent three cycles of coding to identify themes in participant responses to open-ended questions.
    Results: Of 194 fourth-year students, 49 participated in the survey, a response rate of 25%. 79% of participants recalled feeling "unconfident" or "not so confident" in finding information in their first year of study. By their fourth year, all participants indicated they felt "somewhat confident" (53%) or "very confident" (47%). 93% of participants indicated their confidence in searching for information was positively impacted by the mandatory IL curriculum. 90% of participants believed they would use these IL skills after graduation.
    Conclusion: A mandatory curriculum-integrated IL program in undergraduate health sciences positively impacted students' confidence in searching for and finding information. This study indicates the potential benefits of integrating IL instruction into program-wide curricula for undergraduate students.
    DOI:  https://doi.org/10.29173/jchla29808
  9. J Health Commun. 2025 Aug 19. 1-19
    Comparing Large Language Models as Health Literacy Tools: Evaluating and Simplifying Texts on gender-Affirming Surgery.
    Victoria N Yi, Angel P Scialdone, Ann Marie Flusche, Kendall Reitz, Holly C Lewis, William M Tian, Elda Fisher, Kristen Rezak, Ash Patel.
      Patient-facing materials in gender-affirming surgery are often written at a level higher than the NIH-recommended eighth grade reading level for patient education materials. In efforts to make patient resources more accessible, ChatGPT has successfully optimized linguistic content for patients seeking care in various medical fields. This study aims to evaluate and compare the ability of large language models (LLMs) to analyze readability and simplify online patient-facing resources for gender-affirming procedures. Google Incognito searches were performed on 15 terms relating to gender-affirming surgery. The first 20 text results were analyzed for reading level difficulty by an online readability calculator, Readability Scoring System v2.0 (RSS). Eight easily accessible LLMs were used to assess texts for readability and simplify texts to an eighth grade reading level, which were reevaluated by the RSS. Descriptive statistics, t-tests, and one-way ANOVA tests were used for statistical analyses. Online resources were written with a mean reading grade level of 12.66 ± 2.54. Google Gemini was most successful at simplifying texts (8.39 ± 1.49), followed by Anthropic Claude (9.53 ± 1.85) and ChatGPT 4 (10.19 ± 1.83). LLMs had a greater margin of error when assessing readability of feminizing and facial procedures and when simplifying genital procedures (p < .017) Online texts on gender-affirming procedures are written with a readability more challenging than is recommended for patient-facing resources. Certain LLMs were better at simplifying texts than others. Providers should use caution when using LLMs for patient education in gender-affirming care, as they are prone to variability and bias.
    Keywords:  Artificial intelligence; gender-affirming care; gender-affirming surgery; health literacy; transgender health
    DOI:  https://doi.org/10.1080/10810730.2025.2547321
  10. Cureus. 2025 Aug;17(8): e90242
    Comparative Assessment of Large Language Model Outputs and NHS Patient Information in Oral Medicine.
    Jamila Tukur Jido, Ahmed Al-Wizni, Mariam Alkateb, Noor Salem.
      Background Artificial intelligence (AI) and large language models (LLMs) offer transformative potential in healthcare communication, with the National Health Service (NHS) Long Term Plan envisioning digital tools to support accessible, patient-centred information. However, whether LLM-generated health materials are sufficiently readable for patient use remains uncertain, particularly in oral medicine, where conditions like xerostomia, oral candidiasis, and sialolithiasis are common. Objective This study compared the readability of patient information leaflets generated by three LLMs with the NHS UK patient leaflets on common oral medicine conditions, to assess their suitability for public health communication. Methods A cross-sectional analysis was conducted, in which each LLM was prompted to produce patient leaflets for xerostomia, oral candidiasis, and sialolithiasis. Outputs were compared to the NHS UK leaflets on identical topics. Texts were analysed using established readability metrics: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Gunning Fog Index. Results were summarised descriptively without formal statistical testing due to the exploratory study design. Results The NHS UK leaflets consistently demonstrated superior readability across all conditions and metrics, with lower FKGL scores (5.9-6.3) and higher FRES scores (70.5-72.4), indicating suitability for readers aged 11-14 years (Key Stage 3). Among LLMs, ChatGPT produced the most readable outputs, with FKGL scores ranging from 6.8 to 7.2. DeepSeek outputs were moderately more complex (FKGL: 8.3-8.7), while Gemini generated the most complex texts (FKGL: 9.7-10.2), often exceeding recommended reading levels for patient materials. Conclusion While LLMs, especially ChatGPT, show promise in generating patient information, their outputs remain less readable than professionally authored NHS materials. Given that nearly half of UK adults may struggle with complex health texts, the higher reading levels required for LLM-generated content could impede patient understanding and exacerbate health inequalities. As AI becomes more integrated into healthcare communication, ensuring that AI-generated materials meet established readability standards is essential to support equitable, patient-centred care.
    Keywords:  artificial intelligence; health literacy; large language models; national health service; nhs guidelines; patient education; readability measures
    DOI:  https://doi.org/10.7759/cureus.90242
  11. Iran J Nurs Midwifery Res. 2025 Jul-Aug;30(4):30(4): 544-552
    Health Information-Seeking Behavior of Menopausal Women: An Online Survey of Iranian Menopausal Women.
    Masoomeh Latifi, Nader Alishan Karami, Sepideh Miraj, Leili Allahbakhshian, Forough Rahimi, Afsaneh Karbasi.
       Background: Understanding the health information behavior of menopausal women can be effective on their health awareness and improving self-care and preventive health practices. This study aimed to gain deeper insights into the health information-seeking behavior of Iranian menopausal women.
    Materials and Methods: This descriptive-analytical cross-sectional study was conducted in 2023. Data were collected over 2 months, from June 15 to August 15, 2023. The participants were Iranian women aged 50 years and older at the time of the survey. An online questionnaire was developed and distributed through social media to assess the health information-seeking behavior of menopausal women. A total of 4,608 women, selected through cluster sampling, completed the questionnaire. The data were analyzed using SPSS-23 software using the Chi-square test.
    Results: The primary information needs of women during menopause were related to cancer, other diseases, and diagnostic methods. In addition, social networks and friends were the most commonly used information sources among menopausal women. The main barriers to accessing information were the lack of knowledge about information sources, difficulties in obtaining accurate and reliable information, and insufficient information across various sources.
    Conclusions: The findings of this study indicated that women required various types of information, particularly regarding cancers, clinical symptoms of menopause, and nonhormonal treatments. Given the increasing use of social networks, training can be conducted through these platforms and further efforts are necessary to provide more reliable and comprehensive information via these media.
    Keywords:  Information sources; menopause; needs assessment; women
    DOI:  https://doi.org/10.4103/ijnmr.ijnmr_27_24
  12. J Adv Nurs. 2025 Aug 19.
    'Living Well With a PICC at Home': Co-Design and Evaluation of a Peripherally Inserted Central Catheter (PICC) Booklet.
    Rebecca Sharp, Qunyan Xu, Robyn Pumpa, Nadia Corsini, Julie Marker, Jodie Altschwager, Alanna Ortmann, Lisa Turner, Lili Jin, Amanda Ullman, Adrian Esterman.
       AIM: To co-design an information booklet to support adults living with a peripherally inserted central catheter (PICC) at home.
    DESIGN: A sequential, mixed-method approach using Boyd's co-design framework.
    METHOD: A diverse project team led co-design of a PICC booklet based on preferences and PICC management strategies identified through content analysis of interviews with 15 Australian health consumers (January 2022-March 2023) using a qualitative descriptive approach. A draft booklet was developed, reviewed by the team and prototyped, which was evaluated by consumer participants and an external nurse panel, with readability assessed using Flesch Reading Ease and Flesch-Kincaid scores. The project team reviewed feedback, reached consensus on changes and collaborated with designers to produce the final booklet.
    RESULTS: Consumer participant strategies were classified into four categories: enhancing coping, taking responsibility and understanding information, modifying life and accessing supports. Two pre-determined categories were included (participant recommendations for PICC education resource content and top tips for living with a PICC). The prototype was evaluated as highly relevant (3.9/4), comprehensive (3.8/4) with an appropriate writing style and visual appeal. Readability scores were 76.1/100 (Flesch Reading Ease) and 6.9 (Flesch-Kincaid grade level). The final booklet contained 13 sections.
    CONCLUSION: A co-design approach successfully created a novel PICC information booklet with positive evaluations.
    IMPLICATIONS: Provides understanding of consumer information preferences to inform PICC education and offers insight into consumer-developed strategies and knowledge for living with a PICC.
    IMPACT: A co-designed PICC information booklet based on consumer knowledge may augment nursing education for people living with a PICC to improve outcomes.
    REPORTING METHOD: The qualitative component follows the EQUATOR network COREQ guideline. A co-design research reporting standard is not available.
    PATIENT OR PUBLIC CONTRIBUTION: A consumer representative was a member of the project team and contributed to study design, interpretation of findings and development of the booklet and manuscript.
    Keywords:  cancer; co‐design; health consumer information preferences; health consumer knowledge; health consumers; home treatment; infection; peripherally inserted central catheter; self‐efficacy
    DOI:  https://doi.org/10.1111/jan.70146
  13. BMC Public Health. 2025 Aug 16. 25(1): 2800
    Access to healthy ınformatıon: the ınteractıon of medıa lıteracy and health lıteracy.
    Hatice Mutlu, Gözde Bozkurt, Gökten Öngel, Yağmur Gümüşboğa.
       OBJECTIVE: Grounded in the Health Belief Model (HBM) and Media System Dependency Theory (MSDT), this study examines the relationship between media literacy and health literacy in the Turkish context, where digital media plays an increasingly dominant role in disseminating health-related information. It focuses on how individuals access, evaluate, and utilize health information, analyzing the effects of media literacy sub-dimensions-access, analysis, evaluation, and communication-on health literacy levels. The study aims to determine how media-based health information influences individuals' decision-making processes and their ability to make informed health choices.
    METHODS: A survey-based quantitative research approach was employed with 485 participants from Turkey, of which 477 valid responses were analyzed. The Turkish Health Literacy Scale (TSOY-32) measured health literacy, while the Media Literacy Skills Scale assessed media literacy. The Generalized Ordered Logit Model (GOLM) was applied to examine the effects of media literacy on health literacy.
    RESULTS: Descriptive statistics, correlation analysis, and hypothesis testing were conducted. Media literacy levels significantly influence health literacy levels (p < 0.05). The access sub-dimension increased the likelihood of improving health literacy by 1.90 times (p < 0.05). The analysis sub-dimension significantly enhanced health literacy by 2.23 times (p < 0.05). The evaluation sub-dimension had a statistically significant effect on health literacy (p < 0.05). The communication sub-dimension supports individuals in sharing health information and making informed decisions (p < 0.05). Health recommendations disseminated through media significantly impact health literacy levels (p < 0.05). Trust in media-based health information had a weak but positive correlation with health literacy (r = 0.2097, p < 0.05). Education level was positively correlated with health literacy, while income level showed no significant effect. These findings suggest that enhancing media literacy skills, particularly in access and analysis, can meaningfully improve individuals' capacity to navigate complex health information landscapes and foster more informed health behaviors.
    CONCLUSION: The study highlights the crucial role of media literacy in improving health literacy and preventing misinformation. Access and analysis skills are particularly vital in enabling individuals to obtain accurate health information and avoid misleading content. Conducted within the Turkish sociocultural context, where digital media usage is high and misinformation about health is prevalent, the findings offer valuable insights for national health promotion strategies. Policymakers and educators should integrate media literacy into educational curricula, launch public awareness campaigns to combat misinformation, and develop strategies to enhance individuals' critical thinking skills regarding health information. This study is limited by its cross-sectional design and reliance on self-reported data. Future research should employ longitudinal methods and explore the long-term impact of media and health literacy across different demographic groups and contexts.
    Keywords:  Generalized ordered logitmodel; Health literacy; Media literacy
    DOI:  https://doi.org/10.1186/s12889-025-24022-2
This page is being maintained by Thomas Krichel. It was last updated on 2025‒08‒25 at 10:23.