Public attitudes towards the preventability of transport and non-transport related injuries: Can a social marketing campaign make a difference?
Bibliographic record
Abstract
Substantial efforts devoted to decreasing the burden of transport-related injuries (TRIs) in Canada, including public awareness campaigns aiming to influence attitudes and behaviors, may lead the public to perceive other types of injuries differently. This study examined the relationship between public perception of the preventability of injuries and the type of injury (TRIs vs. non-transport unintentional injuries (NTUIs)); and assessed whether exposure to a social marketing campaign ( Preventable ) influenced this association. A cross-sectional study design employed survey data collected by Preventable between 2015 and 2016 from 1501 British Columbians aged 25–54 years. A multiple linear regression model was applied to examine the relationship between the type of injury (TRIs vs. NTUIs) and attitudes towards preventability, controlling for socio-demographic variables. Exposure to the campaign was tested as an effect modifier. On a scale from 1 to 10, respondents perceived TRIs to be 1.08 points more preventable than NTUIs (95% CI: 1.00 to 1.16, p -value < 0.0001). Campaign-exposed participants scored 0.31 points higher on preventability of injuries overall (95% CI: 0.16 to 0.47, p -value < 0.0001); and recorded a smaller difference between the perceived preventability of TRIs and NTUIs, relative to those not exposed to the campaign (B = −0.163, 95% CI: –0.28 to −0.04, p -value = 0.008). While respondents believed that most injuries are preventable, exposure to considerable road traffic interventions in Canada may have influenced public attitudes towards a higher perceived preventability of TRIs. Social marketing may be a useful tool to emphasize the preventability of all injuries to further reduce their burden in Canada.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".