A Comparative Study of Financial Support and Resilience of Self-Employed people in Sweden and Canada
Bibliographic record
Abstract
Abstract Background Globally the COVID-19 pandemic presented major difficulties for self-employed people such as financial decline, restrictions and business closures. A plethora of financial support measures was rolled out worldwide to support them, but there is a lack of research looking at the effectiveness of the policy measures on small businesses. The aim of the study was to compare policies addressing government financial support in Sweden and Canada to get an understanding of how different governmental financial support measures enhanched self-employed people's resilience and improved their chances to manage the pandemic. In addition, individual interviews with Swedish and Canadian self-employed people were conducted to get an understanding of how they experienced the support measures and how these measures factored into their resilience during and after the restrictions had ended. Methods We conducted a mixed-method study encompassing document analysis and semi-structured interviews with self-employed people in Sweden and Canada. The constant comparative method guided the data analysis and documentary and interview data were analysed together. Three categories were formed: Welfare protection and effects, Self-employed well-being and Agility during COVID-19, based on their ability to represent the overall sense of the phenomena. Results Key results were that self-employed people in both countries unable to telework, were less resilient during the pandemic due to financial problems, restrictions and lockdowns, and that this negatively affected their well-being. Conclusions Potential future policy responses in Sweden and Canada to support self-employed people during crises or adversity should consider the diversity among small businesses and tailor programmes towards viable businesses in greatest need of support, such as those in businesses unable to telework. Key messages • Self-employed people in Sweden and Canada unable to telework, were less resilient during the pandemic due to financial problems, restrictions and lockdowns. • Future policy responses to support self-employed people during adversity should tailor programmes towards viable businesses in greatest need of support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".