Trajectories of Well‐Being and Burnout Among Health and Social Services Community Workers During the COVID‐19 Pandemic: A Longitudinal Study of Job Demands and Resources
Bibliographic record
Abstract
Health and social services nonprofit workers face important occupational challenges that were exacerbated by the COVID-19 pandemic and that may have impacted their psychological health during that time. In Quebec, Canada, many of these workers are employed in community-based organizations providing essential services to vulnerable populations. This longitudinal study examined the temporal evolution of well-being and burnout, as well as their associated predictors, among health and social services community workers across three time points: November 2019 to January 2020 (Time 1), June to September 2020 (Time 2), and January to March 2021 (Time 3). Findings revealed significant decreases in well-being and increases in burnout over time. Reduced opportunities to use one's strengths at work was associated with declining well-being, while increased overcommitment was associated with rising burnout. Contrary to expectations, perceived workload and dissatisfaction with pay decreased during the pandemic. Similarly, higher baseline resources and demands were unexpectedly associated with a greater decline in well-being and increase in burnout. This is one of the few studies to have longitudinally examined the well-being and burnout of health and social services community workers. The results show that, among these workers in Quebec, in a crisis situation such as COVID-19, well-being decreases while exhaustion increases. Strengths used at work and overcommitment seem to be important variables to consider in explaining these results, and such insights can be used for developing targeted interventions to support the psychological health of these essential workers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".