Working from home: Impacts on mental and physical health of Canadian employees during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has drastically changed various aspects of our lives, including how we work. Since the start of the pandemic, numerous organizations in Canada have mandated their employees to work from home (WFH) on a full-time basis. The rapid rise in the number of remote workers and the possibility for WFH continuing in the future signifies the importance of understanding the impacts of WFH on employee well-being over the course of the pandemic in Canada. We present the findings of two surveys (initial and 6-month follow-up) to investigate the effects of mandatory WFH arrangements on worker health during the COVID-19 pandemic in Canada. We examined the changes in employees’ mental and physical health. Initial survey was completed between October 2020 and December 2020 (n = 1617); follow-up survey was completed between May 2021 and June 2021 (n = 382). Our analyses showed significant changes in various aspects of employee mental and physical health. Burnout, stress, general mental health, and job insecurity levels significantly decreased between the two time periods. Work-related sedentary behaviour reduced over time; however, the average proportion of time spent sitting during work hours was more than 80% in both surveys. Employees received more help and feedback from their colleagues and experienced a better sense of community with their co-workers over time. The findings can inform workers and organizations on the mental and physical health effects of mandatory WFH arrangements. By understanding the impacts of WFH, employers can develop effective strategies and implement policies that help protect workers’ health and well-being.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".