Canadian Workers’ Well-Being During the Beginning of the COVID-19 Pandemic: A Latent Profile Analysis
Bibliographic record
Abstract
Abstract To explore workers’ well-being during COVID-19, researchers have primarily utilized variable-centered approaches (e.g., regression) focusing on describing workers’ general level of well-being. Given the diversity of factors that may have impacted workers’ well-being during the pandemic, focusing on such well-being trends do not provide sufficient insight into the different lived well-being experiences during the pandemic. Moreover, positive well-being in workers’ general lives and work has been understudied in such complex public health crises. To address these issues, we use latent profile analysis, a person-centered analysis, to explore the diverse well-being realities Canadian workers (employed before COVID-19 or working at the time of the survey) experienced at the beginning of COVID-19. Canadian workers (N = 510) were surveyed between May 20-27th, 2020, on positive (meaning in life, flourishing, thriving at work) and negative (distress, stress, impaired productivity, troublesome symptoms at work) well-being indicators, as well as on factors that may be associated with experiencing different well-being profiles. Five well-being profiles emerged: moderately prospering, prospering, moderately suffering, suffering, and mixed. Factors at the self- (gender, age, disability status, trait resilience), social- (marital status, family functioning, having children at home), workplace- (some employment statuses and work industries, financial strain, job security), and pandemic-related (perceived vulnerability to COVID-19, social distancing) ecological levels predicted profile membership. Recommendations for employers, policymakers, and 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".