Prevalence and risk factors of frailty in people experiencing homelessness: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The experience of homelessness has been associated with premature ageing and an earlier onset of geriatric syndromes. Identification of frailty and appropriate intervention, may help improve health outcomes for people experiencing homelessness (PEH). This review aimed to identify prevalence, use of screening tools and risk factors for frailty in PEH. METHOD: A systematic review, conducted and reported following the PRISMA checklist, was undertaken investigating the prevalence and risk factors of frailty among PEH. Searches were conducted in Ovid MEDLINE, PsycInfo, Web of Science and CINAHL from inception to July 2024. A meta-analysis examining prevalence of frailty and pre-frailty was completed with a narrative synthesis of related risk factors. RESULTS: A total of 1672 articles were screened for eligibility and 11 studies were included, containing 1017 participants from seven countries. Six different screening tools were employed to detect frailty in the included studies. The range of frailty prevalence was 16-70 % and pre-frailty prevalence was 18-60 %. The pooled frailty prevalence from studies employing the Fried Criteria was 39 % (95 % CI 15-66); the Clinical Frailty Scale: 37 % (95 % CI 24-51); the Edmonton Frailty Scale: 53 % (95 % CI 44-63); and the Tilburg Fraily Indicator: 31 % (95 % CI 8-60). High heterogeneity was observed between the studies. Identified risk factors for developing frailty in PEH included being female, increased years spent homeless, and drug addiction. CONCLUSION: This study highlights a high prevalence of frailty and pre-frailty in PEH. The identified risk factors illustrate potential areas to target interventions to reverse frailty. Future research should focus on the role of screening for frailty in PEH and developing appropriate frailty detection tools in this group.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".