Leadership Style as a Predictor of Employee Safety Performance in the Oil and Gas Industry.docx
Bibliographic record
Abstract
<p>The specific problem of this research was it was unknown if/to what extent safety-specific leadership style predicted employee safety performance in the oil and gas industry. The purpose of this research was to examine if/to what extent the safety-specific transformational leadership style of managers predicted employee safety performance in the oil and gas industry in southeast Saskatchewan, Canada. This research's methodology was quantitative, and the research design was simple linear regression. The researcher employed a convenience sampling method and invited 41 business organizations that provided products and services to the oil and gas industry and the business organizations which actively explored, extracted, produced, refined, and transported the oil and gas energy in southeast Saskatchewan, Canada. This research used 89 valid anonymous responses from 32 business organizations in the data analysis. The statistical test of the simple linear regression showed that managers’ safety-specific transformational leadership styles in the oil and gas industry in southeast Saskatchewan, Canada, could significantly predict employees' safety performance. This research has broad implications since 32 business organizations offered multiple products and services to the local oil and gas industry, including construction, transportation, welding, equipment maintenance, and services. Also, this research has broad generalizability, significant Cronbach's alpha values for measuring instruments, a medium to large effect size, and higher confidence in the findings. This research encourages the oilfield industry to promote and grow more safety-specific transformational leaders for higher employee safety performance excellence. </p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".