Loneliness and social support as key contributors to burnout among Canadians workers in the third wave of the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: COVID-19 has dramatically affected Western Society's relationship with work and contributed to increased worker burnout. Existing studies on burnout have mostly emphasized workplace culture, leadership, and employee engagement as key contributors to burnout. In this cross-sectional study, we examine the associations between Malach-Pines Short Burnout Measure (MPSBM) scores and participant's self reported personal characteristics, financial strain, workplace conditions, work-life balance, and social inclusion among Canadians living during the third wave of the COVID-19 pandemic. METHODS: To identify the most salient correlates of burnout, Canadian residents, aged 16+, were recruited using paid social media advertisements in French and English to complete a cross-sectional study. Multivariable linear regression and dominance analysis identified the most salient correlates of MPSBM scores. Exposure variables included demographic factors, financial strain, workplace conditions, work-life balance, social support, and loneliness. RESULTS: Among 486 participants, family social support (adjusted β = -0.14, 95%CI = -0.23, -0.05), emotional loneliness (adjusted β = 0.26, 95% CI = 0.18, 0.35), insufficient sleep (adjusted β = 0.38, 95% CI = 0.16, 0.60) and "me time" (adjusted β = 0.22, 95% CI = 0.03, 0.42), and indicators of financial security (e.g., owning vs renting; adjusted β = -0.36, 95% CI = -0.54, -0.17; insufficient pay: adjusted β = -0.36, 95% CI = -0.54, -0.17) were key burnout indicators. People with a bachelor's degree (vs ≤high school diploma; adjusted β = 0.29, 95% CI = 0.01, 0.58) also had higher burnout scores. CONCLUSION: Interventions addressing workplace culture, leadership, and other proximal workplace stressors, while important, are likely insufficient to meet the needs of workers. Our findings suggest that broader, holistic multicomponent approaches that address multiple upstream dimensions of health-including mental health-are likely necessary to prevent and reduce burnout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".