A systematic review of the use and effectiveness of social media in child health
Bibliographic record
Abstract
BACKGROUND: Social media use is highly prevalent among children, youth, and their caregivers, and its use in healthcare is being explored. The objective of this study was to conduct a systematic review to determine: 1) for what purposes social media is being used in child health and its effectiveness; and 2) the attributes of social media tools that may explain how they are or are not effective. METHODS: We searched Medline, CENTRAL, ERIC, PubMed, CINAHL, Academic Search Complete, Alt Health Watch, Health Source, Communication and Mass Media Complete, Web of Knowledge, and Proquest Dissertation and Theses Database from 2000-2013. We included primary research that evaluated the use of a social media tool, and targeted children, youth, or their families or caregivers. Quality assessment was conducted on all included analytic studies using tools specific to different quantitative designs. RESULTS: We identified 25 studies relevant to child health. The majority targeted adolescents (64%), evaluated social media for health promotion (52%), and used discussion forums (68%). Most often, social media was included as a component of a complex intervention (64%). Due to heterogeneity in conditions, tools, and outcomes, results were not pooled across studies. Attributes of social media perceived to be effective included its use as a distraction in younger children, and its ability to facilitate communication between peers among adolescents. While most authors presented positive conclusions about the social media tool being studied (80%), there is little high quality evidence of improved outcomes to support this claim. CONCLUSIONS: This comprehensive review demonstrates that social media is being used for a variety of conditions and purposes in child health. The findings provide a foundation from which clinicians and researchers can build in the future by identifying tools that have been developed, describing how they have been used, and isolating components that have been effective.
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".