Preventable: a social marketing campaign to prevent injuries in British Columbia, Canada
Bibliographic record
Abstract
Background Injuries are the leading killer of Canadians in the prime of their lives. On average, over 1300 British Columbians die and 27 000 are hospitalised because of injury every year. Formative evaluation revealed 76% of BC residents considered injuries a serious problem; 72% considered injuries to be inevitable and that a social marketing campaign could contribute to injury prevention in BC. Purpose Through a province-wide, multi-partner collaboration, the purpose was to determine the efficacy of a social marketing campaign to change awareness, attitudes, self-reported behaviours and to significantly reduce the number and severity of injuries among BC residents aged 25–55. Methods A multi-year, multi-faceted campaign, focused on what people can do to prevent injury was developed. Utilising TV, Radio, print, guerilla events and social media, the campaign launched in June 2009. Results 2 million BC customers (≈50% of BC population) were reached each week and over 100 million media impressions were generated. 50 000 residents visited http://www.preventable.ca Campaign recall increased 45% between June and December 2009. Ads were considered informative, relevant, credible and generated self-reflection; there was no advertising fatigue. Significant positive shifts (5–10%; p<0.05) were observed in attitudes and self-reported behaviours, and a significant reduction (p<0.05) in injury deaths was associated with the campaign period 2009–2010. Significance/Contribution to the Field A well-developed injury prevention social marketing campaign based upon input from, and discussion with the target audience, can result in significant changes in attitudes and behaviours, and is associated with significantly reduced injury mortality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".