How does informal employment affect health and health equity? Emerging gaps in research from a scoping review and modified e-Delphi survey
Bibliographic record
Abstract
Abstract Introduction This article reports on the results from a scoping review and a modified e-Delphi survey with experts which aimed to synthesize existing knowledge and identify research gaps on the health and health equity implications of informal employment in both low- and middle-income countries (LMICs) and high-income countries (HICs). Methods The scoping review included peer-reviewed articles published online between January 2015 and December 2019 in English. Additionally, a modified e-Delphi survey with experts was conducted to validate our findings from the scoping review and receive feedback on additional research and policy gaps. We drew on micro- and macro-level frameworks on employment relations and health inequities developed by the Employment Conditions Knowledge Network to synthesize and analyze existing literature. Results A total of 540 articles were screened, and 57 met the eligibility criteria for this scoping review study, including 36 on micro-level research, 19 on macro-level research, and 13 on policy intervention research. Most of the included studies were conducted in LMICs while the research interest in informal work and health has increased globally. Findings from existing literature on the health and health equity implications of informal employment are mixed: informal employment does not necessarily lead to poorer health outcomes than formal employment. Although all informal workers share some fundamental vulnerabilities, including harmful working conditions and limited access to health and social protections, the related health implications vary according to the sub-groups of workers (e.g., gender) and the country context (e.g., types of welfare state or labour market). In the modified e-Delphi survey, participants showed a high level of agreement on a lack of consensus on the definition of informal employment, the usefulness of the concept of informal employment, the need for more comparative policy research, qualitative health research, and research on the intersection between gender and informal employment. Conclusions Our results clearly identify the need for more research to further understand the various mechanisms through which informal employment affects health in different countries and for different groups of informal workers.
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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 | 0.269 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".