Health services gaps experienced by non-standard workers in Ontario, Canada: Policy implications
Bibliographic record
Abstract
Abstract Background While the Canadian universal health system provides access to basic services, key health benefits are employer dependent. Given that non-standard workers (NSWs) only rarely have access to such benefits they have increased vulnerability to the many insecurities derived from their precarious employment, as clearly seen during the pandemic. The growing problem of non-standard work and workers’ heightened risk for health status deterioration, followed by a possible accentuation of health inequities, is a population health concern. This study summarizes several health services gaps experienced by NSWs and discusses policy implications and possible solutions. Methods From January to July 2021, we conducted semi-structured interviews with a purposive sample of 40 NSWs in Ontario, Canada, part of a larger mixed-methods six-country study, including three European countries. The target population consisted of workers aged 25 to 55 who, at the time of the survey, were in non-standard employment or lost their job due to the COVID-19 pandemic. Results Our findings highlight complex physical and mental health problems and an overall high burden of disease facing NSWs during the pandemic as linked to a combination of constant stress and worry arising from their employment insecurity, the limited and inconsistent income available to cover their basic needs, and the inadequate and unsafe working conditions they are afraid to challenge. Despite their increased health needs, given that specialized health services are not available to them for free they face financial barriers in accessing much needed health services that could help improve their health status and as a result, delay seeking care or avoid it altogether. Conclusions Sustainable multi sectorial policy solutions are needed including the adoption of relevant labour market legislation and increases in social and health expenditures along with re-adjustments in the ways in which health services are delivered. Key messages • During the pandemic non-standard workers in Ontario, Canada experienced complex health problems and, despite increased health needs, encountered barriers in accessing specialized health services. • The growing problem of non-standard work and workers’ heightened risk for health status deterioration, followed by a possible accentuation of health inequities, is a population health concern.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".