Help-seeking for mental health problems by employees in the Australian Mining Industry
Bibliographic record
Abstract
BACKGROUND: The current study examined help-seeking behavior for mental health problems of employees in the mining industry. METHODS: The research involved a paper-based survey completed by a cross-section of employees from eight coalmine sites. The research aimed to investigate the frequency of contact with professional and non-professional sources of support, and to determine the socio-demographic and workplace factors associated. RESULTS: A total of 1,457 employees participated, of which, 46.6 % of participants reported contact with support to discuss their own mental health within the preceding 12 months. Hierarchical logistic regression revealed a significant contribution of workplace variables, with job security and satisfaction with work significantly associated with help-seeking behavior. CONCLUSIONS: The results provide an insight into the help-seeking behaviour of mining employees, providing useful information to guide mental health workplace program development for the mining industry, and male-dominated industry more broadly.
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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 | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".