The elephant in the room: How personal and workplace traits shape role stress in the construction industry
Bibliographic record
Abstract
Geographical regions shape workplace stress, as each location presents distinct environmental, cultural, and socioeconomic conditions. These regional variations contribute to varying stress levels among workers depending on their unique conditions. This study explored the effects of personal and workplace traits on the role stress of the construction workforce in the South Asian region. Data were collected through a cross-sectional survey involving 274 project-level personnel from ten construction companies in Sri Lanka. Factor analysis validated regression analysis was performed to identify the effects of personal and workplace traits on role stress. The results highlighted the significance of age, education level, and organization tenure in shaping role stress. Specifically, the increasing age was associated with decreased role overload and work-family conflict. A higher level of education was linked to a greater likelihood of experiencing role conflict. Extended organization tenure was found to decrease role ambiguity but increase role conflict and work-family conflict among construction project employees. These findings hold implications for shaping organizational policies aimed at improving employee well-being. By incorporating these insights, organizations can develop strategies that foster a more supportive and healthier work environment. • The influence of personal traits on role stress was assessed. • The effect of workplace traits on role stress was evaluated. • Role ambiguity and role conflict in the construction industry were explored. • Role overload and work-family conflict-related insights were obtained. • Age, education level, and organization tenure affect role stress.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".