Private sector service workers' well-being before and during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Workers attending to the essential functions of society have been most affected by COVID-19, but the well-being of workers outside the health care sector has scarcely been documented. We describe well-being profiles of Finnish blue-collar workers in private sector services and changes in their well-being during the COVID-19 pandemic. Methods Altogether 6345 members of the Service Union United provided cross-sectional pre-COVID data in 2019, and 2702 provided follow-up data on health-related indicators in November 2020. Job industry-specific profiles (retail, hospitality, and property maintenance) and change patterns were analysed. Regression models appropriate for different response types with a random intercept and time component were used. Results Before COVID-19, the well-being profile − food security, body mass index, alcohol use, smoking, and self-perceived health and adequacy of income − was worse among service workers than the population average and it varied by industry sector. During the first year of COVID-19 self-perceived health deteriorated (OR 0.78, 95% CI 0.70–0.87). The proportion of severely food insecure fell from a third to a quarter (OR for improvement 2.66, 95% CI 2.37–2.99). Slight improvements were observed in heavy episodic drinking, smoking, and self-perceived adequacy of income. Employees in property maintenance were the most vulnerable regarding well-being profile and COVID-19-related changes. Conclusion COVID-19 caused divergent changes, including improved food security and deteriorated self-perceived health. Workers with the lowest socioeconomic profile and those facing job uncertainty were the most vulnerable to adverse outcomes. Provision of support to these groups is essential in welfare policy considerations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".