355 PREDICTORS OF HOME SUPPORT SERVICES AND THE CONSEQUENCES OF MISMATCH BETWEEN ALLOCATED AND RECEIVED SERVICES IN COGNITIVELY-IMPAIRED OLDER ADULTS
Bibliographic record
Abstract
Abstract Background Home support services aim to support older people to remain at home. Despite substantial investment in home support hours (€600 million), this has not translated into increased carers on the ground for older people. We aimed to report patterns of home support service utilisation in older patients with memory problems, and identify any mis-matches between allocated and received hours, and the impact on patients and caregivers. Methods Retrospective analysis of consecutive patients referred to community geriatric clinic from January 2021 to May 2022. 95/104 patients who were identified were suitable for inclusion. Results Participants had a median age of 82 (IQR 78-86) of whom 57% were female (n=54). 80% (n=76) were frail (CFS ≥4), with 82% dependent for IADLs (Lawton-Brody IADL Scale ≤6). Median MOCA score was 18, with 44% having moderate to severe cognitive impairment (MOCA ≤17). 40% of patients lived with alone (n=38). 52% (n=49) received formal home supports while 80% (n=76) had an informal carer. 37% (n=18) had a mismatch between hours allocated and hours received. There was a significant difference between median hours of care allocated (7) and median hours of care received (5), p <0.001. Increasing age and frailty, worsening cognitive and functional impairment and living status (living alone) predicted allocation of home supports. Patients who lived with family members were 3 times more likely not to receive allocated hours (OR 3.84 (95% CI 1.2–13.7)) Conclusion In this vulnerable population with cognitive and functional decline, just over half received formal home support hours. A large proportion experienced significant mismatch between allocated and received hours. Family and informal caregivers often have to fill gaps, adding to existing carer strain. Future models of home support should prioritise early intervention for people with IADL loss to remain independent at home and broaden of the scope of practice of carers to facilitate 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".