A Web Tool to Help Counter the Spread of Misinformation and Fake News: Pre-Post Study Among Medical Students to Increase Digital Health Literacy
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic was accompanied by the spread of uncontrolled health information and fake news, which also quickly became an infodemic. Emergency communication is a challenge for public health institutions to engage the public during disease outbreaks. Health professionals need a high level of digital health literacy (DHL) to cope with difficulties; therefore, efforts should be made to address this issue starting from undergraduate medical students. OBJECTIVE: The aim of this study was to investigate the DHL skills of Italian medical students and the effectiveness of an informatics course offered by the University of Florence (Italy). This course focuses on assessing the quality of medical information using the "dottoremaeveroche" (DMEVC) web resource offered by the Italian National Federation of Orders of Surgeons and Dentists, and on health information management. METHODS: A pre-post study was conducted at the University of Florence between November and December 2020. First-year medical students participated in a web-based survey before and after attending the informatics course. The DHL level was self-assessed using the eHealth Literacy Scale for Italy (IT-eHEALS) tool and questions about the features and quality of the resources. All responses were rated on a 5-point Likert scale. Change in the perception of skills was assessed using the Wilcoxon test. RESULTS: A total of 341 students participated in the survey at the beginning of the informatics course (women: n=211, 61.9%; mean age 19.8, SD 2.0) and 217 of them (64.2%) completed the survey at the end of the course. At the first assessment, the DHL level was moderate, with a mean total score of the IT-eHEALS of 2.9 (SD 0.9). Students felt confident about finding health-related information on the internet (mean score of 3.4, SD 1.1), whereas they doubted the usefulness of the information they received (mean score of 2.0, SD 1.0). All scores improved significantly in the second assessment. The overall mean score of the IT-eHEALS significantly increased (P<.001) to 4.2 (SD 0.6). The item with the highest score related to recognizing the quality of health information (mean score of 4.5, SD 0.7), whereas confidence in the practical application of the information received remained the lowest (mean of 3.7, SD 1.1) despite improvement. Almost all students (94.5%) valued the DMEVC as an educational tool. CONCLUSIONS: The DMEVC tool was effective in improving medical students' DHL skills. Effective tools and resources such as the DMEVC website should be used in public health communication to facilitate access to validated evidence and understanding of health recommendations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".