Assessing health disparities faced by female paid domestic workers in Peru before, during, and after the COVID-19 pandemic
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».