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Record W4404591545 · doi:10.1136/spcare-2024-hunc.22

O-22 Frailty behind bars – assessing and managing frailty in a Scottish prison

2024· article· en· W4404591545 on OpenAlexaboutno aff
Sally Boa, Louise Laing

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonComputer scienceGerontologyComputer securityCriminologyPsychologyMedicine

Abstract

fetched live from OpenAlex

Background As the prison population ages (Scottish Government. Scottish prison population statistics 2021–22.), it is important to understand and address the needs of people living with frailty in prison. A screening programme we introduced in our local prison found 10% of people aged 50 and over had moderate or severe frailty. Aims To develop, introduce and evaluate a comprehensive assessment tool and Multi-Disciplinary Meeting (MDT) discussion process to identify and address unmet needs in people in prison living with moderate or severe frailty as identified on the Edmonton Frail Scale (EFS). Methods Based on the Comprehensive Geriatric Assessment and the Health Improvement Scotland Falls and Frailty Tool we developed our own tool, the PRISON ABCD to holistically assess domains of need in people in prison with frailty. The tool looks at physical health, medications, environment, functioning, nutrition, sensory needs, mental health, cognition, mobility and future care planning. Any areas of challenge or unmet need are discussed at a monthly Frailty and Palliative Care MDT within the prison to identify solutions and further actions that are required. Results The rehabilitation team complete the PRISON ABCD with those screened as moderately or severely frail on the EFS. This is then discussed at MDT which is attended by representatives from prison primary care, Scottish Prison Service, local hospice, chaplaincy, social carers, social work, mental health and rehab teams with an average attendance of 13. So far there have been 6 MDTs with 27 patient discussions taking place. Multiple areas of need have been identified across all domains of the PRISON ABCD tool with actions taken to address those needs. Conclusion The PRISON ABCD tool can be used to holistically assess needs in people in prison living with frailty. Frailty MDTs allow discussion of unmet needs and identification of actions to improve care for people living with frailty in prison.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.068
GPT teacher head0.433
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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