Social Media in Oral Health Education: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The literature on the use of social media in oral health education has grown in recent years; however, the research activity on this topic has not been comprehensibly summarised. This scoping review aimed to map the available literature on students' and faculty's use of social media in oral health education across the platforms. METHODS: This review was guided by Arksey and O'Malley's scoping review framework and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for scoping reviews (PRISMA-ScR). Seven databases were searched to include literature until October 2023. Studies were included if they were published in English and focused on using social media in oral health education. Two independent reviewers screened for article eligibility and extracted the relevant data. RESULTS: The review included 40 articles published between January 2008 and October 2023. Most studies used quantitative approaches, did not specify the study design, were noninterventional and reported on undergraduate dental students' use of social media. Included studies centred on patterns of use, views and actual effectiveness of social media. YouTube emerged as the most frequently used platform, followed by Podcast, Facebook and WhatsApp. CONCLUSIONS: The use of social media in oral health education was found to be useful based on direct and indirect outcome measures. However, robust research designs are required to further evaluate the impact of social media on oral health education.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".