Canadian Workers’ Well-Being During the Beginning of the COVID-19 Pandemic: A Latent Profile Analysis
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it