Best Practices for Designing and Testing Behavioral and Health Communication Interventions for Delivery in Private Facebook Groups: Tutorial
Bibliographic record
Abstract
Facebook, the most popular social media platform in the United States, is used by 239 million US adults, which represents 71% of the population. Not only do most US adults use Facebook but they also spend an average of 40 minutes per day on the platform. Due to Facebook's reach and ease of use, it is increasingly being used as a modality for delivering behavioral and health communication interventions. Typically, a Facebook-delivered intervention involves creating a private group to deliver intervention content for participants to engage with asynchronously. In many interventions, a counselor is present to facilitate discussions and provide feedback and support. Studies of Facebook-delivered interventions have been conducted on a variety of topics, and they vary widely in terms of the intervention content used in the group, use of human counselors, group size, engagement, and other characteristics. In addition, results vary widely and may depend on how well the intervention was executed and the degree to which it elicited engagement among participants. Best practices for designing and delivering behavioral intervention content for asynchronous delivery in Facebook groups are lacking, as are best practices for engaging participants via this modality. In this tutorial, we propose best practices for the use of private Facebook groups for delivery and testing the efficacy of behavioral or health communication interventions, including converting traditional intervention content into Facebook posts; creating protocols for onboarding, counseling, engagement, and data management; designing and branding intervention content; and using engagement data to optimize engagement and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".