Perceived impact of rotating shift work on health and wellbeing among underground workers
Bibliographic record
Abstract
Workers in the mining industry are exposed to several risks and hazards in the underground environment. Shift work is a common working arrangement in the mining industry and is associated with many adverse health outcomes. In Canada, there is limited research between rotating shift work in the minerals industry and the effects on health and wellbeing of workers, especially utilizing a qualitative design. This thesis aimed to understand the impact of rotating shift work on perceived health and wellbeing among some underground workers in Sudbury, Ontario. This qualitative descriptive study utilized individual, semistructured interviews with a sample of underground workers (n = 12) employed in Sudbury, Ontario. Interviews were digitally recorded, transcribed verbatim, and analyzed using Braun and Clarke’s 2006 version of thematic analysis. Participants in this study perceived both advantages and disadvantages of working on a rotating shift schedule in relation to their health and wellbeing. Final themes that emerged from the data included: strong preference for the night shift, challenges associated with impact on personal wellbeing, advantages and disadvantages on work environment wellbeing, benefits and challenges of wellbeing external to work, strategies for coping with shift work, family advantages, challenges with partner relationships, challenges and opportunities in relationships with children, and strategies used to protect family wellbeing. The findings of this study may influence future research studies using a quantitative or mixed-method design, and larger samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".