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Record W2809323413

Exploring perceptions of the effect of psychosocial hazards on workers' mental health

2017· article· en· W2809323413 on OpenAlexaboutno aff
Zsuzsanna Kerekes, Michel Larivière, Crislee Dignard, Behdin Nowrouzi‐Kia

Bibliographic record

VenueEuropean Health Psychologist · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialMental healthThematic analysisAutonomyHazardPopulationOccupational safety and healthPsychologyFocus groupPsychological interventionQualitative researchMedicineEnvironmental healthNursingApplied psychologyGerontologyPsychiatryBusiness
DOInot available

Abstract

fetched live from OpenAlex

Background: Previous literature has demonstrated that mining present significant hazards to workers (ILO, 2010, Gyekye, 2003, Amponsah-Tawiah et al, 2013) both mentally and physically. This presentation will consider some of the more controllable psychosocial hazards, defined by Leka and Cox (2010), in a population of mining workers with the additional objectives; 1) better understanding the relationship between the various hazardous factors, and 2) suggesting what could be targeted to improve workers’ mental health and safety from a mental health promotion point of view. Methods: Using qualitative methodologies (focus groups and individual interviews), a heterogeneous sample of participants (n=31) were recruited. These participants were chosen using random sampling strategy from a mining company in Ontario, Canada. A thematic analysis was used to explore perceptions of the effect of psychosocial hazard on workers' mental health. Findings: Work schedule, rotation, and shiftwork were listed among priorities highlighted by the workers to improve their mental health and well-being. Shiftwork was identified as a major occupational risk as well as a significant influence on work-family balance. Discussion: Some degree of control and autonomy over work schedules may prevent or mitigate deleterious occupational health outcomes and positively influence family and community life. The presentation will offer commentary on the usefulness of qualitative methodologies in occupational health research to improve health promotion strategies.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.268
GPT teacher head0.547
Teacher spread0.279 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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