Exploring the Quality of Work Environment at Saudi Aerospace Engineering Industries (SAEI)
Bibliographic record
Abstract
This study aims to evaluate the Quality of Work Environment (QWE) in Aircraft Maintenance Sector of Saudi Aerospace Engineering Industries (SAEI). It covers safety climate (safety, hazard, and injury), employee satisfaction about their jobs and employee satisfaction about management practices. For that purpose, 314 questionnaires were collected and analyzed. The study revealed that SAEI employees have neutral evaluations regarding safety climate in the organization and have neutral evaluations regarding their jobs at SAEI as well. On the other hand, the overall values statically indicate that SAEI employees are unsatisfied regarding SAEI management practices. In conclusion, SAEI employees are unsatisfied about the quality of work environment in general with overall median equal 2 and 95% of confidence. The majority of respondents (60.1%) were between unsatisfied and strongly unsatisfied regarding the QWE. Also, the study indicated that there were statistically significant differences in the employees’ evaluation regarding the QWE according to their job grades, job title, and their departments. These differences can be concluded as following; employees with higher grades were more satisfied with QWE at SAEI, managers, instructors, and auditors were more satisfied with QWE at SAEI and finally TQA employees were the most satisfied employees with QWE at SAEI while Hangar employees were the most unsatisfied. The study suggests some practical recommendations based on the outcomes of this study.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".