Use of seat belts in rural Alberta: an observational analysis.
Bibliographic record
Abstract
OBJECTIVE: This paper details an observational study that estimates rates for wearing seat belts in rural Alberta and compares them with rates derived from a similar study conducted in 1999. METHOD: Direct observations of drivers and front-seat passengers of 72,593 light-duty vehicles were carried out at 334 survey locations in communities with populations of fewer than 25,000, throughout northern, central and southern Alberta. In addition to seat belt use, information collected included vehicle type, gender of drivers and passengers and, at intersections controlled by a stop sign, whether or not the vehicle came to a complete stop. RESULTS: The results indicate that in 2001 in rural Alberta the estimated proportion of driver and front-seat passengers of light-duty vehicles using seat belts was 76.1%. When compared with 1999 data, this represents a 6.9% increase in seat belt wearing rates. The data was desegregated further to show differential wearing rates between drivers of different vehicle types, males and females, drivers and passengers, and between those who came to a complete stop at a stop sign and those who did not. The time of day in which data collection took place also had some influence on seat belt wearing rates. DISCUSSION: This study contributes valuable information to programs and initiatives that aim to increase the use of seat belts in rural Alberta.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".