Educational Video Intervention to Improve Health Misinformation Identification on WhatsApp Among Saudi Arabian Population: Pre-Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: Health misinformation can adversely affect individuals' quality of life and increase the risk of mortality. People often fail to assess the content of messages before sharing them on the internet, increasing the spread of misinformation. The problem is exacerbated by the growing variety of digital information environments, especially social media, which presents as an effective platform for spreading misinformation due to its rapid information-sharing capabilities. Educational interventions have been developed to help consumers verify the validity of digital health information. However, tools designed to detect health misinformation on social media content have not been validated. Given the increased use of social media platforms, particularly WhatsApp, it is crucial to develop tools to help consumers assess the credibility of messages and detect misinformation. OBJECTIVE: The main objective of this study is to develop and assess an educational tool aimed at educating consumers about detecting health misinformation on WhatsApp. The secondary objective is to assess the association between demographic factors and knowledge levels. METHODS: The study used a single-arm, pre-post intervention design to evaluate the effectiveness of an educational video in improving participants' ability to detect health-related misinformation in WhatsApp messages. In the first phase, an educational video intervention was developed and validated. In the second phase, participants were invited to complete a web-based survey that consisted of pre-evaluation questions, followed by the educational video intervention. Subsequently, they were asked to answer the same questions as the postevaluation questions. RESULTS: The web-based survey received 485 responses. The completion rate was 99.6% (n=483). Statistically significant associations existed between knowledge level and age, gender, employment, and region of residence (P<.05). The video intervention did elicit a statistically significant change in the participants' abilities to identify misinformation in WhatsApp messages (z=-6.887; P<.001). Viewing the video was associated with increased knowledge about the following concepts: checking the "forwarded" label (P<.001), looking for spelling and grammatical errors (P<.001), analyzing the facts (P=.03), checking links (P=.002, P=.001), and assessing the photos and videos (P<.001). There was a statistically significant difference in knowledge level before and after the intervention (P<.001). CONCLUSIONS: This study developed and evaluated the effectiveness of an educational video intervention to improve health misinformation identification on WhatsApp among the Saudi Arabian population. The results indicate that educational videos can be valuable tools for improving participants' abilities to identify misinformation. The outcomes of this research can contribute to our understanding of what constitutes an effective tool for enhancing health misinformation awareness. Such interventions may be particularly useful in combating misinformation among Arabic-speaking populations on WhatsApp, which may ultimately improve eHealth literacy. Limiting the prevalence and impact of misinformation allows people to make better-informed health decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".