Oil workers and seat-belt wearing behaviour: the northern Alberta context
Bibliographic record
Abstract
OBJECTIVES: Seat-belt wearing rates in the North reflect workers in the oil industry, necessitating sociocultural descriptions on the issue. The objective of this study was to describe how the social context influences oil workers' views of risk and seat-belt wearing behaviour in northern Alberta. STUDY DESIGN: The study design was qualitative research. Focus groups were held with oil workers in three northern Alberta locations. METHODS: Forty-five oil industry workers participated in 3 focus groups held in a different northern Alberta location, each consisting of 15 participants. Focus group discourse was centred on a series of questions that were clustered around the following themes: (1) propensity to take risks; (2) work patterns and workplace routines; (3) driving history and patterns; (4) self-disclosed seat-belt wearing behaviour; and (5) social relationships. RESULTS: Northern oil workers believe that taking safety risks is an essential characteristic of who they are and where they work. Employers demand consecutive number of hours on the job and offer attractive incentives for working overtime that encourages risk-taking. Risk-taking also appears in driving where workers take numerous risks to get home after they have worked 12-hour shifts for 14 consecutive days. Most are situational seat-belt wearers, buckling up in inclement weather, at the presence of numerous logging trucks and the threat of drunk and/or fatigued drivers. Without prompting, northern oil workers consider fatigued driving as the most dangerous driving risk they experience in the north. Nearly every respondent has experienced fatigued driving after completing his last work shift in a 14-day rotation. CONCLUSIONS: Seat-belt wearing initiatives for oil workers during off-work driving should be led by the oil industries. For example, they could support and encourage the police to increase their enforcement, lobby the government for higher penalties, punish their workers who are caught not wearing seat belts and collaborate with local communities to develop programs that will increase awareness of seat-belt wearing. Because workers described fatigued driving as the key risk in the North, oil industries should become engaged in interventions, with seat-belt wearing as a vital component of fatigued driving.
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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 | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".