Interactive social media interventions to promote health equity: an overview of reviews
Bibliographic record
Abstract
INTRODUCTION: Social media use has been increasing in public health and health promotion because it can remove geographic and physical access barriers. However, these interventions also have the potential to increase health inequities for people who do not have access to or do not use social media. In this paper, we aim to assess the effects of interactive social media interventions on health outcomes, behaviour change and health equity. METHODS: We conducted a rapid response overview of systematic reviews. We used a sensitive search strategy to identify systematic reviews and included those that focussed on interventions allowing two-way interaction such as discussion forums, social networks (e.g. Facebook and Twitter), blogging, applications linked to online communities and media sharing. RESULTS: Eleven systematic reviews met our inclusion criteria. Most interventions addressed by the reviews included online discussion boards or similar strategies, either as stand-alone interventions or in combination with other interventions. Seven reviews reported mixed effects on health outcomes and healthy behaviours. We did not find disaggregated analyses across characteristics associated with disadvantage, such as lower socioeconomic status or age. However, some targeted studies reported that social media interventions were effective in specific populations in terms of age, socioeconomic status, ethnicities and place of residence. Four reviews reported qualitative benefits such as satisfaction, finding information and improved social support. CONCLUSION: Social media interventions were effective in certain populations at risk for disadvantage (youth, older adults, low socioeconomic status, rural), which indicates that these interventions may be effective for promoting health equity. However, confirmation of effectiveness would require further study. Several reviews raised the issue of acceptability of social media interventions. Only four studies reported on the level of intervention use and all of these reported low use. More research on established social media platforms with existing social networks is needed, particularly in populations at risk for disadvantage, to assess effects on health outcomes and health equity.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".