Bibliographic record
Abstract
Dementia in UK prisons: Failings and solutions?Prison is a place built for the young, fit offender (Newcomen, 2016a), yet older people make up the fastest growing demographic in prisons in the United Kingdom (O'Moore, Czachorowski, Leaman, Peden, & Sturup-Toft, 2018) and in other developed countries (Psick, Ahalt, Brown, & Simon, 2017).When paired with the fact that dementia is one of the most pressing healthcare issues in the United Kingdom (Moll, 2013), it is unsurprising that facing dementia is a serious issue for prisons too.The age change in prisons in part reflects changes in average age of the general population in most developed countries.In 2016, people of over 65 years made up 18% of the total U.K. population, and it is projected that they will account for over a quarter of it within the next 40 years (Storey, 2018).In March 2018, 16% of the adult prison population in England and Wales was over 50 years old compared with 7% in 2002 (Sturge, 2019).Generally increased life expectancy is not, however, the only explanation among prisoners.There has also been a rise in historical sex offence proceedings and prison sentences have been getting longer (Di Lorito, Völlm, & Dening, 2018; Ministry of Justice, 2018;Munday, Leaman, & O'Moore, 2017).To compound this, 'old' in prison comes, on average, around 10-15 years earlier than in the general population (Di Lorito et al., 2018; Enggist, Møller, Galea, & Udesen, 2014;Munday et al., 2017).This is attributed to reasons including poverty, inadequate access to healthcare, alcohol, smoking, illicit drug use, as well as psycho-social factors including familial separation and the contemplation of long periods of time in incarceration (Moll, 2013).A widely used figure to demarcate 'old age' in prison is 50 years (Enggist et al., 2014;Munday et al., 2017).Older prisoners, therefore, are probably even more likely than general population peers to have complex physical and mental health needs (Di Lorito et al., 2018; Enggist et al., 2014), including dementia related needs.How well can prisons cope with all this?
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".