Trajectories of Canadian Workers’ Well-Being During the Onset of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Research regarding workers’ well-being over time during COVID-19 has primarily used variable-centered approaches (e.g., ANOVA) to explore changes in negative well-being. However, variable-centered approaches provide insufficient information on the different well-being experiences that diverse workers may have experienced during COVID-19. Furthermore, researchers have understudied positive well-being in workers’ general lives and work during COVID-19. We used latent trajectory analysis, a person-centered analysis, to explore diverse well-being trajectories Canadian workers experienced during the first few months of COVID-19 across distress, flourishing, presenteeism, and thriving at work measures. We hypothesized that: H1) Intragroup differences would be present on each well-being indicator at study onset; H2) Different longitudinal trajectories would emerge for each well-being indicator (i.e., some workers’ scores would get better, some would get worse, and some would remain the same); and H3) Factors at different ecological levels (self, social, workplace, pandemic) would predict membership to the different trajectories. Canadian workers (N = 648) were surveyed March 20-27th, April 3rd-10th, and May 20-27th of 2020. Depending on the well-being indicator, and supporting H1, three to five well-being trajectories were identified. Providing some support for H2, distress and presenteeism trajectories improved over time or stayed stagnant; flourishing and thriving at work trajectories worsened or stayed stagnant. Providing some support for H3, self- (gender, age, disability status, trait resilience), social- (family functioning), workplace- (employment status, financial strain, sense of job security), and pandemic-related (perceived vulnerability to COVID-19) factors significantly predicted well-being trajectory membership. Recommendations for diverse stakeholders (e.g., employers, mental health organizations) 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".