A Formative Evaluation of a Social Media Campaign to Reduce Adolescent Dating Violence
Bibliographic record
Abstract
BACKGROUND: The Emory Jane Fonda Center implemented the Start Strong Atlanta social marketing campaign, "Keep It Strong ATL", in 2007 to promote the development of healthy adolescent relationships and to foster the prevention of adolescent dating abuse among 11-14 year olds. OBJECTIVE: A formative evaluation was conducted to understand whether messages directed at the target audience were relevant to the program's relationship promotion and violence prevention goals, and whether the "Web 2.0" social media channels of communication (Facebook, Twitter, YouTube, Flickr, Tumblr, and Pinterest) were reaching the intended audience. METHODS: Mixed methodologies included qualitative interviews and a key informant focus group, a cross-sectional survey, and web analytics. Qualitative data were analyzed using constant comparative methodology informed by grounded theory. Descriptive statistics were generated from survey data, and web analytics provided user information and traffic patterns. RESULTS: Results indicated that the Keep It Strong ATL social marketing campaign was a valuable community resource that had potential for broader scope and greater reach. The evaluation team learned the importance of reaching adolescents through Web 2.0 platforms, and the need for message dissemination via peers. Survey results indicated that Facebook (ranked 6.5 out of 8) was the highest rated social media outlet overall, and exhibited greatest appeal and most frequent visits, yet analytics revealed that only 3.5% of "likes" were from the target audience. These results indicate that the social media campaign is reaching predominantly women (76.5% of viewership) who are outside of the target age range of 11-14 years. CONCLUSIONS: While the social media campaign was successfully launched, the findings indicate the need for a more focused selection of communication channels, timing of media updates to maximize visibility, balancing message tone and delivery, and incorporating differentiated messaging for the target audiences. Collaboration with parents and community partners is also emphasized in order to expand the campaign's reach and create more channels to disseminate relationship promotion and dating violence prevention messaging to the intended audience.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".