Towards a better understanding of First Nations communities and drinking and driving
Bibliographic record
Abstract
OBJECTIVES: First Nations young people are over-represented in fatal alcohol-related crashes, necessitating culturally sensitive data that sheds light on this major health issue. The objective of this study was to understand why young First Nations drivers, aged 18 to 29 years old, become involved in drinking and driving as normal behavior displayed through socio-cultural patterns. STUDY DESIGN AND METHODS: Sixty-five First Nations respondents were individually interviewed in nine Alberta locations. Semi-structured interviews, focusing on socio-cultural patterns, norms and community ethos affecting alcohol consumption, drinking and driving, and drinking and driving interventions, were used. RESULTS: Community norms play a significant role in the drinking and driving behaviors of First Nations people. First Nations communities experience reckless driving, neighbors with alcohol and drug abuse problems, violence, economic disparity, boredom and racism, all of which contribute to responses of alcohol abuse and drinking and driving. Both are considered to be normal, community-endorsed behaviors, reflecting situational needs and ready-at-hand usage. Furthermore, the grid of rural roadways is an important contributor to drinking and driving and a community sense of practical living. Also of importance was the finding that young people embraced their parents' alcohol-related problem behaviors on the basis that "what is okay for the parents is okay for me." Finally, First Nations young people believe in personally stopping a drunken person from driving, but the risk of community censure, social discomfort and risk of physical and verbal abuse mitigate against them taking action. CONCLUSIONS: Living in First Nations communities is socially complex, highly emotionally charged, and peer-pressured. Drinking and driving and alcohol abuse amongst First Nations people reflect the community social structure, daily pressures and norms of behavior. Hence, to reduce drinking and driving casualties amongst First Nations young people, intervention strategies must address systemic issues, namely local people's social realities, norms, as well as local and peer relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".