Entrepreneurial Stress and Mental Well-being: Pre- and Post-COVID-19 Comparative Analysis of SMEs in Canada
Bibliographic record
Abstract
There is no conclusive evidence to compare pre- and post-COVID-19 data to measure the performance of SMEs through the lens of mental well-being. The goal of this paper is to contribute to the existing knowledge through a comparative lens, especially considering entrepreneurial stress. This research examined the entrepreneurial stress and mental well-being of entrepreneurs in Canadian SMEs, specifically pre- and post-COVID-19. The PRISMA framework, based on the philosophy of interpretivism, was applied and 37 articles were taken into consideration for this research. The findings confirm that there is a significant increase in the entrepreneurial stress affecting the mental well-being and overall performance of Canadian SMEs, especially during and after COVID-19. Furthermore, this research confirmed that everyone’s reaction to and precautions taken against entrepreneurial stress vary. Interestingly, distinct stressors such as finances, family, and work–life imbalance have evidently increased. However, the magnitude of these stressors varies for individuals. Similarly, the consequences also differ; however, emotional symptoms, followed by physical and behavioural symptoms, have noticeably been exhibited by entrepreneurs both pre- and post-COVID, while performance among SMEs has dwindled, which has brought about a higher number of entrepreneurship closures. This manuscript contributes to the existing literature from multiple facets, such as (i) through the comparative lens, (ii) enriching the literature from an advanced economy, (iii) expanding upon the theory of entrepreneurial stress, and (iv) shedding light on the importance of entrepreneurs’ mental well-being. The manuscript also provides practical implications to entrepreneurs to deal with distinct types of stressors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".