Response to the Article “Addressing Health Misinformation: Promoting Accurate and Reliable Information”
Bibliographic record
Abstract
Dear Editor, I read with great interest the article titled “Addressing Health Misinformation: Promoting Accurate and Reliable Information” published in 2024 Volume 12 Issue 3 of Archives of Medicine and Health Sciences in the pages 432–435.[1] This is a timely and important article. The authors have put forward important strategies to combat health misinformation using a whole of society approach. This has been a topic of interest to me for over the two decades. In the early years of the 21st century, the major source of health information for patients was through an online search using search engines and through websites. At a medical college in Nepal, undergraduate medical students were taught to access and assess the quality of health information on the internet.[2] At that time several organizations including the Health on the Net foundation, the Canadian Health Network and others had developed criteria to assess the quality of internet health information. Some of the organizations and sites are no longer functional due to various reasons. Internet information can be biased and may be of variable quality. New quality checklists have been developed. Consumers who search for health information online often do so without professional guidance and empowering patients to evaluate online content by themselves could be an effective approach.[3] The quality evaluation instrument to be used should be available to consumers, require assessment of a limited number of elements, and must be easily readable. MedlinePlus has produced a checklist to evaluate the quality of health information[4] and a DISCERN questionnaire is also available.[5] The MedlinePlus checklist assesses criteria like the provider (Who created the website and why? How can they be contacted, if required?), funding (How is the site funded? Does the site offer commercials? Does the funder influence the information presented?), quality (Who created the content? What are the qualifications of this person? Has it been checked by experts? Is the content up to date? Does the site avoid emotional claims?) and privacy (does the site collect personal information? Does it mention how it will be used? Are users comfortable using the site?) among others. Whether the source mention about uncertainty and bias and possible conflicts of interest can also be noted. Today short and long videos and artificial intelligence (AI) may be used as information sources. YouTube is a popular source of videos. An article mentions the three criteria to measure the quality of YouTube videos.[6] These are expert-driven measures, popularity-driven measures, and heuristic-driven measures. Quality of content, view count, opinion of health professionals, adequate duration, public ratings, adequate tags, title and information, comprehensive narrative, description of evidence-based practices, technical quality, credentials, and suitability as a teaching tool were among the different criteria mentioned. Peer review by social networks has been recommended. However, these measures can be easily manipulated, and many videos may not meet the quality criteria. The patient education materials assessment tool was developed to assess the understandability of print and audiovisual patient information.[7] TikTok is a popular social media site for short videos. A recent study found that several health information videos were posted by nonmedical influencers on TikTok and these had several drawbacks.[8] TikTok may not be a good source of health information. Generative AI may also be used to answer health queries. Search engines are now incorporating generative AI into search results. A recent article provides some tips for using generative AI.[9] AI can be used to provide context or education. Some AI platforms (especially the free ones) may not be updated in the real time. Users should consider the source of the information and be sceptical. During the last two decades, there has been an explosion of content on the internet and accessing credible and quality-assured content requires knowledge and effort. Medical students, postgraduates, and doctors should be aware of different quality criteria and be able to advise patients and the public about the quality of internet health information. Students should be educated on the criteria to be considered when evaluating online health information including who created the information/site/video, what are the person/s qualifications, what are the references cited, when the resource was produced, when it was last updated, who has reviewed the information, and whether the creator/s have any conflict of interest. For videos, adequate duration, comprehensive narrative, and the technical quality should also be considered. An interactive plenary followed by a hands-on session is recommended for students. These sessions may have to conducted periodically during the course. Students should also be educated on communicating this information to patients. The authors have briefly addressed the consequences of health misinformation in their well-presented article.[1] Recent advances require additions to teaching learners about assessing the quality of health information and carrying out patient education to include short and long videos and AI sources. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".