An evidence-based method for targeting an abusive head trauma prevention media campaign and its evaluation
Bibliographic record
Abstract
BACKGROUND: A triple-dose abusive head trauma (AHT) prevention program (Period of PURPLE Crying) was implemented. The third dose consisted of an education media campaign. The study objectives were to describe the qualitative and spatial methods developed to target AHT prevention and to evaluate this campaign. METHODS: A questionnaire on the level of importance of factors, rated on a 7-point Likert scale, was distributed to a panel of experts to determine the best advertising locations. Ranked factors were used to create weights for statistical modeling and mapping within a Geographic Information Systems to determine optimal ad locations. The media campaign was evaluated via a telephone survey of randomly selected households. RESULTS: The survey found locations of new families, high population density, and high percentage of lone parents to be the most important factors for selecting billboard sites. Spatial analysis revealed six areas that ranked highest in our factors. Five billboards, four media posters, and six transit shelters were selected for our advertisements. A population-based telephone survey revealed that 23% of respondents knew the campaign. Nearly half (42%) heard the radio public service announcements, and 9% saw billboards. CONCLUSION: Extending primary prevention efforts to the public helps to create a cultural change in the way inconsolable crying, the trigger for AHT, is viewed. With the use of ranked factors and Geographic Information Systems, geographic locations with high visibility and specific risk factors for AHT were identified for targeting the campaign, facilitating the likelihood that our message was reaching the population in greatest need.
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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 | 0.178 | 0.295 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.024 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".