Elements That Underpin the Design, Development, and Evaluation of Social Media Health Interventions: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Social media use has grown tremendously over the years. Given the volume and diversity of people on social media and the amount of information being exchanged, it is perhaps unsurprising that social media is being used as an avenue to disseminate and deliver health interventions. There exists an opportunity for social media health interventions to make a positive impact on health. However, there is a need to understand more about the ways in which these interventions are designed, developed, and evaluated. This scoping protocol will review the current state of this field by charting the elements that drive the design, development, and evaluation of these interventions. This includes charting models, frameworks, and rationales for the interventions, as well as the platforms being used, and the health behaviors being targeted. This intention of this scoping review is to help inform those who wish to develop effective social media health interventions. OBJECTIVE: The objective of this review is to map the elements that drive the design, development, and evaluation of social media health interventions. We define "social media health intervention" as interventions that make use of social media platforms to disseminate or deliver health-related information and educational initiatives to the public. We will seek to chart the elements that drive the design, development, and delivery of such interventions, including their platforms and targeted health behaviors. METHODS: The methodological framework for this review is guided by Arksey and O'Malley and enhancements by later studies. We will search relevant literature from 9 databases: (1) PubMed, (2) PsycINFO, (3) EMBASE, (4) Web of Science, (5) Scopus, (6) CINAHL, (7) ERIC, (8) MEDLINE, and (9) Google Scholar. The literature will be screened by at least two reviewers in 2 stages: (1) title/abstract screening against the eligibility criteria; and (2) eligible articles will then undergo a full-text screening. Data will be charted using the data charting tool developed by the authors. RESULTS: The results of this study will be presented in a final scoping review paper, divided into 2 sections. The first section will describe the search strategy and study selection process and will contain the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart. The second section will provide key details pertaining to the review objective and question. CONCLUSIONS: This review will help guide scholars looking to build social media health interventions toward evidence-based practices in design and evaluation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31911.
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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 | 0.188 | 0.213 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.070 | 0.023 |
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".