Reversal of the British trends in place of death: Time series analysis 2004–2010
Bibliographic record
Abstract
BACKGROUND: Increased attention is being paid to the place where people die with a view to providing choice and adequately planning care for terminally ill patients. Secular trends towards an institutionalised dying have been reported in Britain and other developed world regions. AIM: This study aimed to examine British national trends in place of death from 2004 to 2010. DESIGN AND SETTING: Descriptive analysis of death registration data from the Office for National Statistics, representing all 3,525,564 decedents in England and Wales from 2004 to 2010. RESULTS: There was a slow but steady increase in the proportion of deaths at home, from 18.3% in 2004 to 20.8% in 2010. Absolute numbers of home deaths increased by 9.1%, whilst overall numbers of deaths decreased by 3.8%. The rise in home deaths was more pronounced in cancer, happened for both genders and across all age groups, except for those younger than 14 years and for those aged 65-84, but only up to 2006. The rise was more evident when ageing was accounted for (age-gender standardised proportions of home deaths increased from 20.6% to 23.5%). CONCLUSIONS: Following trends in the USA and Canada, dying is also shifting to people's homes in Britain. Home deaths increased for the first time since 1974 amongst people aged 85 years and over. There is an urgent need across nations for comparative evidence on the outcomes and the costs of dying at home.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".