Examining the Influence of Demographics on Workers’ Experiences of Psychosocial Risk in the Canadian Construction and Extractive Industries
Bibliographic record
Abstract
Workers in the Canadian construction and extractive industries (CEIs) are exposed to psychosocial risk factors (PRFs) and experience a greater prevalence of mental health issues than the public.Managing PRFs requires the use of risk management theory; however, there is little research on PRFs in the Canadian CEIs, especially as it relates to workers' views.The problem is that the lack of knowledge of the importance of PRFs, from the perspective of Canadian CEI workers, is impairing effective risk management and having a deleterious effect on workers' mental health.The purpose of this quantitative, nonexperimental, correlational study was to examine the relationship between five demographic control factors of age, gender, residence type, employment arrangement, and rotation status, and 15 measures of Canadian CEI workers' perspective of PRFs using the theoretical foundation of risk management theory.Using a crosssectional design, a 90-question survey was administered to 174 workers over the age of 18 to obtain demographic data and scores for the PRFs using the Copenhagen Psychosocial Questionnaire -Canadian version.Analysis of variance was used to compare means across groups to determine if there is a difference in views of PRFs.The findings revealed that while workers' experiences are largely unique, there is often a stark difference between the experiences of workers based on age, gender, and employment arrangement.The study helps to provide a more complete assessment of the nature of PRFs within the CEIs and could help leaders to establish more effective risk management strategies for the betterment of workers, employers, and the broader communities in which they live and operate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".