“Don't Suffer in Silence” — Applying the Integrated Model for Social Marketers to Campaigns Targeting Victims of Domestic Violence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We conducted a review and analysis of multicomponent social marketing campaigns targeting victims of domestic violence, which were gathered from a variety of websites in five English-speaking countries: United States, Canada, United Kingdom, Australia, and New Zealand. We examined the degree to which these campaigns conform to the Integrated Model for Social Marketers developed by Cismaru, Lavack, Hadjistavropoulos, and Dorsch (2008). This model describes the variables salient in each stage of behavioral change and provides a description of the most effective strategies for persuasion. Key recommendations for enhancing future initiatives targeting victims of domestic violence suggest that it is important to emphasize the benefits of changing, as well as to convince victims of domestic violence that they can improve their lives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it