Precision Prevention through Social Media: Report of Four Cases
Bibliographic record
Abstract
BACKGROUND: Precision prevention involves using biological, behavioral, socioeconomic, and epidemiological data to improve health for a particular individual or group. With almost 63% of the global population using social media, these platforms show promise to deliver tailored messaging and personalized interventions to individuals. OBJECTIVES: To describe the personalization elements and behavior components used in a sample of precision prevention interventions delivered through social media. METHODS: To identify examples of cases, a search was done on clinicaltrials.gov, searching for 'other terms: prevention' + 'Intervention/Treatment: social media intervention' + 'study results: With results. The selected cases were described, personalization elements reported, and their adopted intervention components were coded according to the Behavior Change Wheel (BCW) framework. RESULTS: A total of four cases employing personalization in their interventions were identified. Three of these cases targeted women's health. The intervention period varied from two to eight months, with participant commitment ranging from active involvement on five out of seven days to monthly participation. The BCW interventions of persuasion and incentivization, were most frequently utilized, while education and coercion were used sparingly in the selected cases. Notably, none of the four cases reported the use of training, restrictions, or modeling. CONCLUSIONS: Social media has the potential to serve as a tool for digital phenotyping and contribute to the advancement of precision prevention. Challenges include the social media platform set-up and ensuring all ethical considerations are met.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".