COVID-19 en migrantes y minorías étnicas
Bibliographic record
Abstract
Todavía son escasas las publicaciones que analizan los efectos en migrantes o minorías étnicas de la COVID-19 o de las medidas adoptadas para frenar la pandemia, si bien los primeros estudios apuntan a un mayor impacto en poblaciones negras, asiáticas y de minorías étnicas en Reino Unido o en migrantes en México. Además de en las barreras de acceso a la información y a los servicios sanitarios, consideramos prioritario poner el foco de atención en sus condiciones de vida, particularmente las de quienes se encuentran en situaciones de vulnerabilidad o exclusión social. Nos referimos a personas desempleadas o con trabajos precarizados, sin prestaciones sociales, en condiciones de hacinamiento, que pueden están más expuestas al riesgo de infección y a no recibir un tratamiento adecuado. Previsiblemente el confinamiento ha repercutido más negativamente en migrantes en situación administrativa irregular, en víctimas de violencia de género y en quienes no pueden cumplir con las medidas de distanciamiento físico, como personas refugiadas en campamentos o migrantes en infraviviendas y asentamientos chabolistas, sin condiciones higiénicas adecuadas. Recomendaciones como suspender las deportaciones, prorrogar o facilitar permisos de residencia y trabajo, cerrar los centros de detención de personas extranjeras, evacuar a quienes están en cárceles y en campos de refugiados o asentamientos se han aplicado de manera desigual en diferentes países. Solo una fuerte apuesta política por la equidad sanitaria mundial puede garantizar la salud de poblaciones migrantes y de minorías étnicas y su acceso a medidas de protección, información, pruebas médicas y servicios sanitarios.Palabras clave: Migrantes, COVID-19, Grupos Minoritarios, Vulnerabilidad Social, Determinantes Sociales de la Salud. There are still few publications that analyse the effects on migrants or ethnic minorities of COVID-19 or of measures taken to curb this pandemic, although early studies point to a greater impact on black, asian and ethnic minority populations in the UK or on migrants in Mexico. In addition to barriers to access to information and health services, we consider it a priority to focus on their living conditions, particularly those in situations of vulnerability or social exclusion. People who are unemployed or with precarious jobs, without social benefits, in overcrowded conditions, may be more at risk of infection and not receiving adequate treatment. Confinement has predictably more negative impact on migrants in irregular administrative situations, victims of gender-based violence and those unable to comply with physical estrangement measures, such as refugees in camps or migrants under-living and settlements, without adequate hygienic conditions. Recommendations such as suspending deportations, extending or facilitating residence and work permits, closing detention centres for foreign persons, evacuating those in prisons and refugee camps or settlements have been applied unequally in different countries. Only a strong political commitment to global health equity can ensure the health of migrant populations and ethnic minorities, as well as their access to protection measures, information, medical testing and health services.Keywords: Migrants, COVID-19, Minority Groups, Vulnerable Populations, Social Determinants of Health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".