How Supervisors Managed Their OHS Roles with Workers Working from Home During the COVID Epidemic: A Qualitative Study
Bibliographic record
Abstract
During the Covid-19 pandemic, although the experience of workers and managers was examined, the role, experiences and functions of supervisors was relatively underexplored, with no investigation into their changing health and safety responsibilities. This project attempted to fill this gap. Twenty supervisors across Canada were interviewed for an hour. A Framework Method guided the study. We used a conceptual framework of 10 supervisor functions to help direct the data collection, identify codes, manage and organize the data analysis, and identify major themes which were highlighted in the findings. What was found was that since supervisors did not have access to workers’ homes, they could not execute most of their OHS functions. They were obliged to give workers more control over how and when they worked. They Increased their communications with their workers in response to workers’ psychological health concerns. Notably, supervisors reported that they were under extreme stress. A hybrid work environment, with workers sometimes working at home, has become the new norm. Supervisor stress will continue to escalate unless upper management provides more support, supervisors get training on how to deal with the psychological health and safety of workers, and supervisors’ responsibilities are re-defined for at-home workers.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".