21 Assessing frailty in people experiencing homelessness: developing and piloting a tool for hostel staff
Bibliographic record
Abstract
Introduction People experiencing homelessness (PEH) often have complex health and support needs, and are often supported by non-clinical hostel staff. Extreme levels of multimorbidity, premature onset of frailty and geriatric conditions at a young age have been observed in this population. Frailty is characterised by issues like reduced muscle strength and fatigue. It is a multidimensional state of vulnerability arising from a complex interplay of biological, cognitive, and social factors. There is growing recognition of the impact of frailty in this population, but it is not consistently recognised or responded to. Aims This study aimed to develop and pilot a questionnaire in collaboration with homelessness staff and other experts, to screen for frailty and other health and social care needs among PEH residing in hostels. Method Through a consultation process with hostel staff and inclusion health experts, we developed a Frailty and Holistic Needs Questionnaire based on the Edmonton Frail Scale (validated for use by non-clinical staff). We piloted the questionnaire in two hostels, with staff completing the questionnaires with PEH. Staff focus groups explored the acceptability and potential usage of the questionnaire to screen for frailty and advocate for support where needs are identified. Results 70 PEH completed the tool with their keyworkers. Over half of responding PEH (54%) were identified as frail and 21% identified as pre frail. Multiple other health issues were also reported. The questionnaire and its completion were viewed positively by staff and PEH, indicating that it may be an acceptable intervention. Conclusion The screening questionnaire was a feasible intervention to screen for frailty and other health and social care needs among residents of homeless hotels. Impact This intervention has great potential for gathering evidence about health and social care needs to advocate for better access to care for a population that historically struggle to access it.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".