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Record W7043274186

Self-employment, health, illness, and social security among solo self-employed workers

2022· dissertation· en· W7043274186 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Government (linguistics)Social securityWork (physics)PretextPopulation
DOInot available

Abstract

fetched live from OpenAlex

Today’s labour market has changed over time, shifting from full-time, secured, and standard employment relationships to entrepreneurial and precarious working arrangements. Thus, self-employment (SE) has been growing rapidly in recent decades due to globalization, automation, dramatic technological advances, the information revolution, and the recent rise of the ‘gig economy’. More than 60% of workers worldwide are in non-standard employment relationships; hence their employment positions are precarious. This precarity profoundly impacts workers’ health and well-being, undermining the comprehensiveness of social security systems, employment standards, and occupational health and safety policies. The general goal of this research was to focus on the circumstances of solo self-employed (SE’d) workers, investigating how they navigate, experience, and manage their injuries/illness in the context of their work.
\nTo explore this, this dissertation combines three findings’ manuscripts: (i) the first manuscript, based on a scoping review, critically reviewed the peer-reviewed literature focusing on advanced economies to understand how SE’d workers navigate, experience, or manage their injuries and illness when unable to work. The scoping review was a critical interpretive synthesis, following Dixon-Woods et al. (2006). (ii) The second manuscript considered how self-employed people access social support systems when they are not working due to injury and sickness in the two comparable countries of Canada and Australia. This comparative policy analysis adopted ‘interpretive policy analysis’ (Yanow, 2000), which involved analyzing public policies as a form of text or representation of social actions. (iii) Finally, the third manuscript examined how SE’d workers in Ontario, Canada were protected with available social security systems, following illness, injury, and income reduction or loss. Drawing on-depth interviews with 24 solo SE’d people; thematic analysis was conducted based on participant narratives. 
\nFindings revealed that one of the challenges of providing support to SE’d people is derived from unclear definitions of who is SE’d. Thus, based on peer-reviewed literature, this dissertation demystified the conceptualization of SE and explored why people choose SE, including the push and pull factors. The comparative policy analysis revealed that support for SE’d workers following their injury or sickness was barely present in the relevant policies in Australia (NSW) and Canada (Ontario). In both cases, the SE’d workers tended to be homogenized in policy documents and literature as financially prosperous, younger, and highly educated. In this context, this study argues that a significant number of SE’d workers living in both jurisdictions need income support during their absence from work due to injury and sickness. This dissertation also explored the experiences of SE’d workers in Ontario in terms of social security systems that SE’d workers encountered when ill or injured. The study identified several constraints to social security access in this context: premium affordability, information/knowledge gap, lack of SE social support programs, the red tape of bureaucracy, confidence about savings, and lack of trust in the government-regulated system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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