Stress, anxiety, and depression among mining workers: understanding the correlates of mental health and wellbeing
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».