Correlation of Subject Characteristics, Work Stress Levels, and Smoking Patterns among Educational Personnel at X University, Indonesia
Bibliographic record
Abstract
Human resources are among the crucial aspects of an organization, including in higher-education organizations. Educational personnel, a key component of the education system, are prone to work stress, which may trigger smoking behavior. Personal characteristics may also influence smoking behavior. This cross-sectional observational analytic study aimed to analyze the relationship between characteristics, work stress level, and smoking behavior of educational personnel of X University, Indonesia. On 30 April–Mei 2021, subjects were recruited through total sampling based on inclusion and exclusion criteria (n=100, all males). A questionnaire that had been tested for validity and reliability was used to collect data on subject characteristics and behaviors, while DASS-42 was used to measure work stress. Age, education level, length of work, and work stress were the independent variables, while smoking was the dependent variable. Data collected were analyzed univariately and bivariately using the chi-square test, with p<0.05 considered significant. Age, education, and length of work were found to be significantly correlated with smoking (p=0.007, 0.016, and 0.009, respectively). However, stress levels did not correlate with smoking (p=0.786). This suggests that age, education, and length of work significantly influence smoking behavior. It's crucial to interpret these findings with caution, especially considering that all subjects are males, who have been proven less prone to stress than females. This caution is necessary to ensure a comprehensive understanding of the factors influencing smoking behavior among educational personnel.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".