Social Media in Communicating Health Information: An Analysis of Facebook Groups Related to Hypertension
Bibliographic record
Abstract
INTRODUCTION: We studied Facebook groups related to hypertension to characterize their objectives, subject matter, member sizes, geographical boundaries, level of activity, and user-generated content. METHODS: We performed a systematic search among open Facebook groups using the keywords "hypertension," "high blood pressure," "raised blood pressure," and "blood pressure." We extracted relevant data from each group's content and developed a coding and categorizing scheme for the whole data set. Stepwise logistic regression was used to explore factors independently associated with each group's level of activity. RESULTS: We found 187 hypertension-related Facebook groups containing 8,966 members. The main objective of most (59.9%) Facebook groups was to create hypertension awareness, and 11.2% were created primarily to support patients and caregivers. Among the top-displayed, most recent posts (n = 164), 21.3% were focused on product or service promotion, whereas one-fifth of posts were related to hypertension-awareness information. Each Facebook group's level of activity was independently associated with group size (adjusted odds ratio [AOR], 1.02; 95% confidence interval [CI], 1.01-1.03), presence of "likes" on the most recent wall post (AOR, 3.55, 95% CI, 1.41-8.92), and presence of attached files on the group wall (AOR, 5.01, 95% CI, 1.25-20.1). CONCLUSION: The primary objective of most of the hypertension-related Facebook groups observed in this study was awareness creation. Compared with the whole Facebook community, the total number of hypertension-related Facebook groups and their users was small and the groups were less active.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".