Tackling Paradoxes and Double Binds for a Healthier Workplace: Insights from the Early COVID-19 Responses in Quebec and Ontario
Bibliographic record
Abstract
The urgency of managing the COVID-19 health crisis in workplaces led to tensions, work overload, and confusion about preventive measures. This study presents a secondary analysis of qualitative data on paradoxes and double binds (PDBs) experienced by precarious essential workers in Canada who interacted with the public and their supervisors. Based on 13 interviews from a larger qualitative dataset, we examine how workers navigated public health recommendations and organisational demands during the pandemic. Findings reveal multiple organisational and managerial PDBs—both COVID-19-related and pre-existing—that contributed to psychological distress and compromised well-being. We argue that PDBs represent a significant occupational health hazard for precarious workers. Addressing these structural contradictions through proactive management strategies could help mitigate workplace tensions, reduce stress, and enhance resilience in both crisis situations and regular organisational contexts. Our study contributes to occupational health and safety (OHS) by underscoring the risks posed by PDBs and advocating for strategies to support vulnerable workers in navigating conflicting demands.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".