Alcohol and injury in Atlantic Canada: creating a culture of safer consumption
Bibliographic record
Abstract
Alcohol places individuals at increased risk of intentional and unintentional injuries, including suicide, violence, motor vehicle collisions and falls. The culture of alcohol consumption in Atlantic Canada contributes to the burden of alcohol-related injuries in the region. Methods An environmental scan was conducted which involved a literature review and key informant interviews. The literature review included academic and grey literature (1998-present). Key informant interviews were conducted with 18 individuals throughout Atlantic Canada. The data was coded using the qualitative software package NVivo (version 7). Results Atlantic Canadians have rates of hazardous drinking higher than the Canadian average. Results indicated that Atlantic Canadian culture accepts and expects alcohol consumption and is highly tolerant of over-consumption and intoxication. Alcohol consumption increases risk of injury, injury severity and number of body regions injured. The results demonstrated that injuries sustained while under the influence of alcohol are in many cases viewed as humorous or as badges of honour. The culture of alcohol and injury in Atlantic Canada was seen to be a product of heritage and industry, marketing strategies by alcohol companies and policies regarding alcohol pricing, sales, and outlet density. Conclusion The results have demonstrated a need for a comprehensive approach to de-normalising hazardous use of alcohol in Atlantic Canada. Recommendations were developed to address policy, create a supportive cultural environment and to create individual awareness and understanding of the issue.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".