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 \nconsequences of poor mental in the workplace are numerous and well-documented. Despite this, \nmental health research specific to the mining industry remains scarce, especially in Canada \nwhere mining plays a significant economic role. What is more, workers in male-dominated \nindustries have been found to be at greater risk for mood and anxiety disorders, and the limited \nexisting literature depicts higher rates of mental illness among mining workers. This is relevant \nin Canada because the mining industry is a major employer of Canadians. \nObjective: Our research team conducted a study at a large mining company in Ontario, Canada \nto better understand the mental health and wellbeing of their workforce by assessing symptoms \nof various mental health problems and illnesses, as well as work and non-work-related factors \nthat may be associated with these symptoms. As part of this study, my thesis examines the \nprevalence of stress, anxiety, and depression symptoms in this sample of Canadian mine \nworkers, as well as the demographic, health-related, psychosocial, and work-related predictors of \nstress, anxiety, and depression symptoms for these workers. Methods: 2,224 mining workers across 25 worksites at one company in Ontario, Canada \ncompleted a self-reported questionnaire. The survey included assessments of symptoms of stress, \nanxiety, 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, \nsymptom prevalence of anxiety and depression were greater in this workforce than in the general \nworking population of Canada. Significant correlates of these workers’ mental health and wellbeing were grouped into the following 8 categories: individual characteristics, interpersonal \nrelationships, lifestyle, and the overlap between physical and mental health (see Chapter 6), as \nwell as work schedule and demands, effort-reward imbalance and recognition and reward, job \ninsecurity and job satisfaction, and the physical and psychological work environment (see \nChapter 7). Conclusions: Findings are consistent with previous research and confirmed our hypotheses. \nRecommendations for addressing significant predictors of mental health and wellbeing for these \nworkers are presented in Chapter 8.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".