Facebook-Based Social Marketing to Reduce Smoking in Australia’s First Nations Communities: An Analysis of Reach, Shares, and Likes (Preprint)
Bibliographic record
Abstract
BACKGROUND Facebook is widely used by Australia’s First Nations people and has significant potential to promote health. However, evidence-based guidelines for its use in health promotion are lacking. Smoking prevalence among Australia’s First Nations people is nearly 3 times higher than other Australians. Locally designed programs in Aboriginal Community Controlled Health Services (ACCHOs) to reduce smoking often use Facebook. OBJECTIVE This study reports on an analysis of the reach and engagement of Facebook posts with smoking prevention and cessation messages posted by ACCHOs in the Northern Territory, Australia. METHODS Each service posted tobacco control content at least weekly for approximately 6 months. Posts were coded for the following variables: service posted, tailored First Nations Australian content, local or nonlocally produced content, video or nonvideo, communication technique, and emotional appeal. The overall reach, shares, and reactions were calculated. RESULTS Compared with posts developed by the health services, posts with content created by other sources had greater reach (adjusted incident rate ratio [IRR] 1.92, 95% CI 1.03-3.59). Similarly, reactions to posts (IRR 1.89, 95% CI 1.40-2.56) and shared posts (IRR 2.17, 95% CI 1.31-3.61) with content created by other sources also had more reactions, after controlling for reach, as did posts with local First Nations content compared with posts with no First Nations content (IRR 1.71, 95% CI 1.21-2.34). CONCLUSIONS Facebook posts with nonlocally produced content can be an important component of a social media campaign run by local health organizations. With the exception of nonlocally produced content, we did not find a definitive set of characteristics that were clearly associated with reach, shares, and reactions. Beyond reach, shares, and likes, further research is needed to understand the extent that social media content can influence health behavior.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".