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Record W2523673789 · doi:10.1186/s12913-016-1755-1

Help-seeking for mental health problems by employees in the Australian Mining Industry

2016· article· en· W2523673789 on OpenAlexfundno aff
Ross Tynan, Robyn Considine, Jane Rich, Jaelea Skehan, John Wiggers, Terry J. Lewin, Carole James, Kerry Inder, Amanda Baker, Frances Kay‐Lambkin, David Perkins, Brian Kelly

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
FundersAustralian Coal Industry’s Research ProgramMcGill University
KeywordsMental healthHealth informaticsHealth administrationMining industryLogistic regressionJob satisfactionMedicineWork (physics)Public healthNursing researchEnvironmental healthApplied psychologyNursingPsychologyPsychiatrySocial psychologyEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.507
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2016
Admission routes1
Has abstractyes

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