Assessing health disparities faced by female paid domestic workers in Peru before, during, and after the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Female paid domestic workers are among the most vulnerable occupational groups globally, often lacking formal social protection and limited labour rights. The COVID-19 pandemic may have exacerbated these vulnerabilities, yet quantitative evidence from low and middle income countries is scarce. This study examines health disparities in Peru between female paid domestic workers and females employed in the formal service sector before, during, and after the pandemic. METHODS: We used pooled cross sectional data from the Peruvian National Household Survey (ENAHO, 2018-2023). The primary outcomes were self reported illness symptoms and healthcare seeking behaviour. We compared female paid domestic workers to female formal workers in other service occupations-including both personal and nonpersonal services-across three time periods: prepandemic (January 2018 - February 2020), pandemic (March 2020 - October 2022), and postpandemic (November 2022 - December 2023). Analyses involved comparing differences in proportions and conducting Wald tests. We also stratified results by key social determinants of health, including education, ethnicity, age, income, chronic disease status, household head status, and access to labour rights. RESULTS: Female paid domestic workers reported more illness symptoms and sought less healthcare than females working in nonpersonal service roles, especially during the pandemic. The difference in proportions - 5.9 percentage points (pp.) for illness symptoms and 16.5 pp. for healthcare-seeking behaviour- became smaller after one year. There were no significant differences when comparing female paid domestic workers to other personal service workers. Stratified results indicated that outcome differences between female paid domestic workers and female working in non-personal services were wider among household heads, those with chronic conditions, and those with limited access to labour rights. Post-pandemic disparities were especially pronounced among younger females, low-wage earners, and those with less education. CONCLUSION: In Peru, female paid domestic workers experienced persistent health disadvantages before, during, and after the COVID-19 pandemic when compared with females with formal employment. Addressing these disparities requires comprehensive policies that promote formalization and social security coverage to advance progress on Sustainable Development Goal 3.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".