Assessment of Fire Prevention Knowledge and Safety Practices of Car Dealership Employees in Nakhon Si Thammarat, Thailand
Bibliographic record
Abstract
Numerous fires are often started by unsafe actions, including negligence, ignorance, or failure to consider fairly obvious hazards. The aim of this cross-sectional study was to evaluate the knowledge and practices of employees in car dealership centers regarding fire prevention. The sample of this study included 118 participants selected by simple random sampling. The data were processed using SPSS IBM version 28 and analyzed using descriptive and inferential statistics. Most of the participants were males (58.47%) and maintenance technicians (40.68%), with 1-5 years of work experience (61.02%). The majority of the participants had appropriate knowledge about fire prevention as the most of them answered the knowledge questions correctly. Their educational level, age, and work experience were all important significance of their potential fire prevention expertise. A high level of knowledge was reflected in the safety practices of the participants regarding the fire prevention; thus, knowledge is still considered to play an important role for every employee in fire accidents. There is an important need to provide fire safety training for all workers to increase safety practices in timely intervals. In addition, fire prevention systems must be ready, efficient, and safe to prevent losses and to ensure the safety of the employees' and companies' lives and properties.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".