Examining the health and wellness of solo self-employed workers through narratives of precarity: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In recent decades, there has been a significant transformation in the world of work that is characterized by a shift from traditional manufacturing and managerial capitalism, which offered stable full-time employment, to new forms of entrepreneurial capitalism. This new paradigm involves various forms of insecure, contingent, and non-standard work arrangements. Within this context, there has been a noticeable rise in Self-Employed individuals, exhibiting a wide range of -working arrangements. Despite numerous investigations into the factors driving individuals towards Self-Employment and the associated uncertainties and insecurities impacting their lives and job prospects, studies have specifically delved into the connection between the precarious identity of Self-Employed workers and their overall health and well-being. This exploratory study drew on a 'precarity' lens to make contributions to knowledge about Self-Employed workers, aiming to explore how their vulnerable social position might have detrimental effects on their health and well-being. METHODS: Drawing on in-depth interviews with 24 solo Self-Employed people in Ontario (January - July 2021), narrative thematic analysis was conducted based on participants' narratives of their work experiences. The dataset was analyzed with the support of NVIVO qualitative data analysis software to elicit narratives and themes. FINDINGS: The findings showed that people opt into Self-Employment because they prefer flexibility and autonomy in their working life. However, moving forward, in the guise of flexibility, they encounter a life of precarity, in terms of job unsustainability, uncertainties, insecurities, unstable working hours and income, and exclusion from social benefits. As a result, the health and well-being of Self-Employed workers are adversely affected by anger, anomie, and anxiety, bringing forward potential risks for a growing population. CONCLUSION AND IMPLICATIONS: Neoliberalism fabricates a 'precariat' Self-Employed class. This is a social position that is vague, volatile, and contingent, that foreshadows potential threats of the health and wellbeing of a growing population in the changing workforce. The findings in this research facilitate some policy implications and practices at the federal or provincial government level to better support the health and wellbeing of SE'd 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".