Using Knowledge Translation to Craft “Sticky” Social Media Health Messages That Provoke Interest, Raise Awareness, Impart Knowledge, and Inspire Change
Bibliographic record
Abstract
BACKGROUND: In Australia, there is growing use of technology supported knowledge translation (KT) strategies such as social media and mobile apps in health promotion and in Indigenous health. However, little is known about how individuals use technologies and the evidence base for the impact of these health interventions on health behavior change is meager. OBJECTIVE: The objective of our study was to examine how Facebook is used to promote health messages to Indigenous people and discuss how KT can support planning and implementing health messages to ensure chosen strategies are fit for the purpose and achieve impact. METHODS: A desktop audit of health promotion campaigns on smoking prevention and cessation for Australian Indigenous people using Facebook was conducted. RESULTS: Our audit identified 13 out of 21 eligible campaigns that used Facebook. Facebook pages with the highest number of likes (more than 5000) were linked to a website and to other social media applications and demonstrated stickiness characteristics by posting frequently (triggers and unexpected), recruiting sporting or public personalities to promote campaigns (social currency and public), recruiting Indigenous people from the local region (stories and emotion), and sharing stories and experiences based on real-life events (credible and practical value). CONCLUSIONS: KT planning may support campaigns to identify and select KT strategies that are best suited and well-aligned to the campaign's goals, messages, and target audiences. KT planning can also help mitigate unforeseen and expected risks, reduce unwarranted costs and expenses, achieve goals, and limit the peer pressure of using strategies that may not be fit for purpose. One of the main challenges in using KT systems and processes involves coming to an adequate conceptualization of the KT process itself.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".