Influence of the pandemic on the mental health of professional workers
Bibliographic record
Abstract
BACKGROUND: This study focuses on the influence of the pandemic on professional workers from an explicitly comparative perspective. High levels of stress and burnout have been reported among professional workers pre-pandemic, but the pandemic has had unique consequences for certain professional workers. Gender has emerged as a particularly important factor. While the existing research yields important insights of mental health concerns among professional workers, there is a need for more research that examines these impacts empirically, explicitly from a comparative perspective across professions taking gender more fully into consideration. METHODS: This paper undertakes a secondary data analysis of two different pan Canadian sources to address the pandemic impact on professional workers: The Canadian Community Health Survey (2020, 2021) administered by Statistics Canada and the Healthy Professional Worker survey (2021). Across the two datasets, we focused on the following professional workers - academics, accountants, dentists, nurses, physicians and teachers - representing a range of work settings and gender composition. Inferential statistics analyses were conducted to provide prevalence rates of self-perceived worsened mental health since the pandemic and to examine the inter-group differences. RESULTS: Statistical analysis of these two data sources revealed a significant effect of the pandemic on the mental health of professional workers, that there were differences across professional workers and that gender had a notable effect both at the individual and professional level. This included significant differences in self-reported mental health, distress, burnout and presenteeism prior to and during the pandemic, as well as the overall impact of the pandemic on mental health. The high levels of distress and burnout during the pandemic were particularly evident in nursing, teaching, and midwifery - professions where women predominate. CONCLUSIONS: Interventions to address the mental health consequences of the pandemic, including their unique gendered and professional dimensions, should consider the intersecting influences and differences revealed through our analysis. In addition to being gender sensitive, interventions need to take into account the unique circumstances of each profession to better respond to the mental health needs of all genders within each professional group.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".