The Influence of Medical Expertise and Information Search Skills on Medical Information Searching: Comparative Analysis From a Free Data Set
Bibliographic record
Abstract
BACKGROUND: Nowadays, the internet has become the primary source of information for physicians seeking answers to medical questions about their patients before consulting colleagues. However, many websites provide low-quality, unreliable information that lacks scientific validation. Therefore, physicians must develop strong information search skills to locate relevant, accurate, and evidence-based content. However, previous studies have shown that physicians often have poor search skills and struggle to find information on the web, which may have detrimental consequences for patient care. OBJECTIVE: This study aims to determine how medical students and residents searched for medical information on the internet, the quality of the web resources they used (including their nature and credibility), and how they evaluated the reliability of these resources and the answers they provided. Given the importance of domain knowledge (in this case, medicine) and information search skills in the search process, we compared the search behaviors of medical students and residents with those of computer science students. While medical students and residents possess greater medical-related knowledge, computer science students have stronger information search skills. METHODS: A total of 20 students participated in this study: 10 medical students and residents, and 10 computer science students. Data were extracted from a freely accessible data set in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. All participants searched for medical information online to make a diagnosis, select a treatment, and enhance their knowledge of a medical condition-3 primary activities they commonly perform. We analyzed search performance metrics, including search time, the use of medical-related keywords, and the accuracy of the information found, as well as the nature and credibility of web resources used by medical students and residents compared with computer science students. RESULTS: Medical students and residents provided more accurate answers than computer science students without requiring additional time. Their medical expertise also enabled them to better assess the reliability of resources and select high-quality web sources, primarily from hospital websites. However, it is noteworthy that they made limited use of evidence-based tools such as PubMed. CONCLUSIONS: Although medical students and residents generally outperformed computer science students, they did not frequently use evidence-based tools. As previously observed, they may avoid databases due to the risk of encountering too many irrelevant articles and difficulties in applying appropriate filters to locate relevant information. Nevertheless, clinical and practical evidence-based medicine plays a crucial role in updating physicians' knowledge, improving patient care, and enhancing physician-patient relationships. Therefore, information search skills should be an integral part of medical education and continuing professional development for physicians.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".