Leadership Style as a Predictor of Employee Safety Performance in the Oil and Gas Industry
Bibliographic record
Abstract
Some managers experience challenges in addressing workplace safety concerns and employees' needs to enhance worksite safety performance. This quantitative simple linear regression research examined if/to what extent a relationship existed between managers’ safety-specific transformational leadership style and employee safety performance in the oil and gas industry in southeast Saskatchewan, Canada’s oil and gas industry. We used 89 valid anonymous responses from 32 organizations for the data analysis. The statistical test showed managers’ safety-specific transformational leadership styles could significantly predict employees’ safety performance (F(1, 89) = 49.03, p<0.001, R2 = 0.36). Additionally, the curve estimation of the data revealed that about 35.4% to 38.30% of the change in employees’ safety performance was attributed to managers' safety-specific transformational leadership behaviors. This research has broad implications, a medium to large effect size, and a higher confidence level. The findings of this research encourage the oil and gas businesses to promote and grow more safety-specific transformational leaders to attain higher employee safety performance excellence in the industry.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".