La desconocida mortalidad de la población en las residencias de personas mayores de España
Bibliographic record
Abstract
Es necesario conocer la mortalidad de las personas mayores que viven en residencias para evaluar sus determinantes, incluyendo las características estructurales y organizativas de estos centros y su relación con la utilización de servicios sanitarios y sociales. Al querer investigar la mortalidad de la población mayor de 65 años que vive en residencias durante la COVID-19 nos encontramos con la imposibilidad de identificar a las personas fallecidas con domicilio habitual en residencias y, en consecuencia, de conocer el número de defunciones y sus causas. En esta nota de campo describimos esta situación anómala y proponemos una solución: el cumplimiento de la ley que obliga a todos los ciudadanos al empadronamiento en el domicilio habitual, lo que debería ser exigido en el proceso de admisión a una residencia. Se aseguraría así la disponibilidad de los datos necesarios para conocer la mortalidad de la población que reside en una residencia. It seems necessary to assess the mortality of older people living in long-term care homes to examine its determinants, including the structural and organizational characteristics of these centers and their relationship with the use of health and social services. Attempting to investigate the mortality of the population over 65 years of age living in long-term care homes during COVID-19, we were not able to identify those who died at their long-term care home and, consequently, to know their number of deaths and their causes. In this field note, we describe this anomalous situation and propose a solution: compliance with the law that obliges all citizens to register at their usual address, which should be required in the process of admission to a residence. This would ensure the availability of the necessary data to know the mortality of the population residing in a residence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".