Stress, anxiety, and depression among mining workers: understanding the correlates of mental health and wellbeing
Bibliographic record
Abstract
Background: Mental health problems are among the leading causes of disability. The consequences of poor mental in the workplace are numerous and well-documented. Despite this, mental health research specific to the mining industry remains scarce, especially in Canada where mining plays a significant economic role. What is more, workers in male-dominated industries have been found to be at greater risk for mood and anxiety disorders, and the limited existing literature depicts higher rates of mental illness among mining workers. This is relevant in Canada because the mining industry is a major employer of Canadians. Objective: Our research team conducted a study at a large mining company in Ontario, Canada to better understand the mental health and wellbeing of their workforce by assessing symptoms of various mental health problems and illnesses, as well as work and non-work-related factors that may be associated with these symptoms. As part of this study, my thesis examines the prevalence of stress, anxiety, and depression symptoms in this sample of Canadian mine workers, as well as the demographic, health-related, psychosocial, and work-related predictors of stress, anxiety, and depression symptoms for these workers. Methods: 2,224 mining workers across 25 worksites at one company in Ontario, Canada completed a self-reported questionnaire. The survey included assessments of symptoms of stress, anxiety, and depression, demographic questions, and assessments of psychosocial and healthrelated factors associated with stress, anxiety, and depression. Results: While stress levels were found to be comparable to the general working population, symptom prevalence of anxiety and depression were greater in this workforce than in the general working population of Canada. Significant correlates of these workers’ mental health and wellbeing were grouped into the following 8 categories: individual characteristics, interpersonal relationships, lifestyle, and the overlap between physical and mental health (see Chapter 6), as well as work schedule and demands, effort-reward imbalance and recognition and reward, job insecurity and job satisfaction, and the physical and psychological work environment (see Chapter 7). Conclusions: Findings are consistent with previous research and confirmed our hypotheses. Recommendations for addressing significant predictors of mental health and wellbeing for these workers are presented in Chapter 8.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".