Overcoming the unseen: Understanding diverse Canadian workers’ well-being during the beginning of the coronavirus pandemic
Bibliographic record
Abstract
Canadian workers were severely affected by the onset of the coronavirus (COVID-19) pandemic. The rapid spread of the pathogen across Canada resulted in several work-related consequences (e.g., temporary/indefinite layoffs and business closures, mandatory working-from-home) that could have impacted workers’ well-being during this turbulent period. Research available at the start of this dissertation process, and even more recently published studies, primarily explored general trends in workers’ negative well-being during COVID-19, not examining the distinct realities that diverse workers, including more marginalized ones, may have experienced. Across three manuscripts, several theories from diverse disciplines (positive, developmental, community, and industrial-organizational psychology, public health) were married with the objective of exploring diverse workers’ holistic well-being during pandemics and epidemics and the beginning of COVID-19. Informing Manuscripts 2 and 3, Manuscript 1 (via a scoping review including 187 studies) found that positive well-being was experienced frequently or at moderately high levels, and work-related well-being was experienced highly during pandemics and epidemics. Negative well-being was mild to moderate during SARS and COVID-19 but high during other pandemics and epidemics. In Manuscript 2 (using latent profile analysis; N = 510), we found the presence of five distinct well-being realities (moderately prospering, prospering, moderately suffering, suffering, mixed) that Canadian workers experienced during May of 2020 across negative and positive general life and work well-being indicators. Most workers were flourishing to some degree. In Manuscript 3 (using latent trajectory analysis; N = 648), we found that Canadian workers had varying levels of well-being at the onset of COVID-19. Whereas negative well-being improved over time or stayed stagnant, positive well-being worsened or stayed stagnant between March and May of 2020. Most workers were found to be in prospering trajectories. Several factors at distinct ecological levels (self, social, workplace, pandemic) were related to workers’ well-being (Manuscript 1) or predicted membership to well-being profiles (Manuscript 2) and trajectories (Manuscript 3). Several implications for researchers and nonacademic stakeholders (e.g., employers, mental health organizations, policymakers) are discussed.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".