Could Better‐Quality Employment Improve Population Health? Findings From a Scoping Review of Multi‐Dimensional Employment Quality Research and a Proposed Research Direction
Bibliographic record
Abstract
BACKGROUND: Precarious employment, a specific part of the conceptual spectrum of employment quality (EQ), has been established as an important risk to individual and population health and well-being when compared to a standard employment circumstance. There remains a need, however, to explore whether and how EQ might be used as a tool to not only protect but also advance population health and well-being. METHODS: The purposes of this scoping review were to assess the analytic treatment of the multiple dimensions of EQ and the stances researchers take to characterize the state of knowledge of EQ that supports the idea that better EQ is a health-promoting factor. Quantitative, qualitative, and mixed-methods primary studies that included at least three of the seven conceptually-informed EQ dimensions were eligible. Studies were assessed for EQ dimensions represented, how dimensions were treated analytically, the pathogenic, ambivalent, or salutogenic stances used by investigators, and what each might tell us about how to leverage aspects of better-quality employment to improve population health. RESULTS: A total of 78 studies were included; 54 of these treated EQ dimensions in an interrelated way. Of the analytically interrelated studies, none had an explicit salutogenic stance. Some evidence suggests that a handful of EQ types might present an equal or reduced risk of poor health than the standard employment relationship, frequently used as a historic gold standard. CONCLUSION: Research with a salutogenic stance might build our understanding of whether and how employment could be used to advance our collective well-being.
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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 | 0.085 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".