Self-employment, illness, and the social security system: a qualitative study of the experiences of solo self-employed workers in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Today's labor market has changed over time, shifting from mostly full-time, secured, and standard employment relationships to mostly entrepreneurial and precarious working arrangements. Thus, self-employment (SE) has been growing rapidly in recent decades due to globalization, automation, technological advances, and the recent rise of the 'gig' economy, among other factors. Accordingly, more than 60% of workers worldwide are non-standard and precarious. This precarity profoundly impacts workers' health and well-being, undermining the comprehensiveness of social security systems. This study aims to examine the experiences of self-employed (SE'd) workers on how they are protected with available social security systems following illness, injury, and income reduction or loss. METHODS: Drawing on in-depth interviews with 24 solo SE'd people in Ontario (January - July 2021), thematic analysis was conducted based on participants' narratives of experiences with available security systems following illness or injury. The dataset was analyzed using NVIVO qualitative software to elicit narratives and themes. FINDINGS: Three major themes emerged through the narrative analysis: (i) policy-practice (mis)matching, (ii) compromise for a decent life, and (iii) equity in work and benefits. CONCLUSIONS: Meagre government-provided formal supports may adversely impact the health and wellbeing of self-employed workers. This study points to ways that statutory social protection programs should be decoupled from benefits provided by employers. Instead, government can introduce a comprehensive program that may compensate or protect low-income individuals irrespective of employment status.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".