UTILIZATION OF ELDER CARE AMONG PEOPLE LIVING WITH DEMENTIA IN SWEDEN: A REGISTER-BASED STUDY
Bibliographic record
Abstract
Abstract The growing number of people living with dementia (PlwD) implies an increase in the demand for eldercare at different stages of the disease. This study aims to investigate the utilization of eldercare among people with and without dementia in Sweden during the last five years of life and what social-background factors influence the use of eldercare. Data were derived from four linked Swedish national registers comprising all decedents aged 70+ in Sweden as of November 2019 (n=6294). The primary outcome variable was the utilization of eldercare (no care, homecare, residential care). Following the study sample retrospective from death, data analysis was performed using multinomial and linear logistic regression models. Results showed that (1) nearly a quarter of all PlwD did not use any eldercare, primarily people who were newly diagnosed with dementia and living with partners; (2) three out of four PlwD used residential care in the last years of life; and (3) age, gender, and cohabitation status were important social-background factors determining utilization of eldercare for PlwD. This study provides unique insight that many Swedes with a dementia diagnosis do not receive any eldercare and that the utilization of eldercare increases with time since dementia diagnosis. We suggest more research to investigate why a substantial part of PlwD does not have any eldercare at all and what the policy implications of this might be.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".