Examining factors in presenteeism and absenteeism: physical activity rates and mental health related predictors of productivity loss in a mining population
Bibliographic record
Abstract
Canadian companies are estimated to lose $16 billion in work productivity per year from workers \ncalling in sick due to mental health issues (Mercer, 2018). Impacts in work productivity are \ncommonly reflected in rates of absenteeism and presenteeism. This study uses data gathered on \nthe Mining Mental Health Study to evaluate this issue in a Northern Ontario Mining Population \nby identifying predictors of mental health-related and physical health-related productivity loss. \nPreviously receiving mental health treatments or taking mental health medication were the leading \ncauses of mental health-related work productivity loss, while a physical disease diagnosis was the \nleading cause of physical health-related work productivity loss. Depression symptom severity was \nsignificant in predicting instances of both absenteeism and presenteeism. Interventions in this \npopulation should focus on providing resources to lower depression symptom severity and be peerbased, as to help overcome any existing mental health stigma in this male dominated industry \n(Sayers et al., 2019).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".