Feasibility and acceptability of strategies to address mental health and mental ill-health in the Australian coal mining industry
Bibliographic record
Abstract
To evaluate the feasibility, acceptability and effectiveness of implementing a peer-based, multi-component mental health program in the Australian coal mining industry. The multicomponent program included MATES in mining (a peer-based mental health and suicide prevention program) and supervisor training. Eight Australian coal mines participated in the research, with four mines receiving the mental health program. Primary outcome variables including mental health stigma, help-seeking behaviour and perception of the workplace commitment to mental health were measured prior to program implementation, and then again 10 months following using a paper based survey. Process evaluation of the mental health program was measured using a pre-test/post-test survey. MATES in mining 1275 miners participated in the MATES general awareness and connector training. Participants were more confident that they could identify a workmate experiencing mental ill-health; help a workmate, family member or themselves identify where to get support and were more willing to start a conversation with a workmate about their mental health. Supervisor training 117 supervisors completed the supervisor training and were subsequently more confident that they could: identify someone experiencing mental ill-health in the workplace; identify and recommend support services to a person experiencing mental ill-health; and have an effective conversation about performance issues that may be due to mental ill-health. Our findings show evidence to support the use of peer-based mental health programs in the mining industry, and for male-dominated industry more broadly.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".