P-112 Frailty behind bars – identifying frailty in a Scottish prison
Bibliographic record
Abstract
<h3>Background</h3> The prison population in Scotland is ageing (Scottish Government. Scottish Prison Population statistics 2021–22) with increasing numbers of people in prison with significant health issues. There is a need to better understand and address the needs of people living with frailty in prisons. <h3>Aims</h3> To explore the acceptability and feasibility of implementing a frailty screening programme in partnership with the health team at our local prison. <h3>Methods</h3> As part of an ongoing frailty project, all people in prison aged 50 or over at our local prison were offered frailty screening between July 2023 and January 2024. Those who accepted were screened for frailty by the prison primary care team using the Edmonton Frail Scale (EFS). <h3>Results</h3> 242 people in prison aged 50 or over were offered frailty screening. 210 accepted (87%). Of those screened, 24% were found to have some degree of frailty on the EFS (mild/moderate/severe). 10% of those screened had moderate or severe frailty. Frailty was found in both younger and older cohorts - 39% of those aged 65 and over were frail (n=21) compared to 19% of those aged 50–64 years (n=29). For those found to be moderately or severely frail 50% were under 65 and 50% aged 65 and over. <h3>Conclusion</h3> Uptake of frailty screening was high (88%). Almost a quarter of our prison population of over 50s was found to be frail with 10% living with moderate or severe frailty. Frailty screening traditionally looks at those aged 65 or over, but if we had only screened older people in prison, we would have missed 58% of those who were frail which perhaps reflects premature frailty among the prison population.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".