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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".