The various insecurities experienced among non-standard workers across different policy-contexts
Bibliographic record
Abstract
Abstract Background Over the last decades, the prevalence of non-standard employment (NSE) has increased in many countries, with negative implications for worker health and well-being. Research at the micro level, mostly quantitative, has linked NSE with poor health through insecurity. Macro-level studies investigating whether political economic factors buffer the harms of NSE have generated mixed results. This study describes how various types of insecurity are experienced by workers in NSE, in general and during COVID-19, and how this influences their health and well-being, in six countries with different welfare states: Belgium, Canada, Chile, Spain, Sweden and the United States. Methods In-depth interviews with 250 workers in NSE were analysed using a multiple-case study approach and using the welfare state typology as a macro-level framework. Results Despite differences in welfare states, workers in all six countries experienced multiple forms of insecurity as well as relational tension with employers or clients, with clear negative effects on their well-being, in ways that were shaped by broader social inequalities (e.g., related to gender, age, and access to family support). Simultaneously, differences in welfare states were reflected in the level of workers’ exclusion from social protections, the temporality of difficulties they faced in planning their lives (e.g., threats to daily survival or to longer-term life planning), and their ability to derive control from NSE despite the insecurity it created. Workers in less generous welfare states experienced heightened insecurity and greater stress from the COVID-19, but the severity of the health and economic crisis was felt by workers in all study countries. Conclusions This study sheds light on the ways that welfare regimes can support - or fail to support - workers in NSE, and suggests the need in all six countries for stronger state responses to NSE, a pressing social determinant of health. Key messages
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".