Abstract 2828: Caregivers And Their Impact On Inpatient Rehabilitation Efficiency And Effectiveness Amongst Recent Stroke Survivors In An Urbanised Asian Society
Bibliographic record
Abstract
Introduction: Cross-cultural differences could influence the relationship between caregiving and rehabilitation outcomes in stroke survivors between different societies. We aimed to determine independent factors of rehabilitation effectiveness (REs) and rehabilitation efficiency (REy) amongst recent stroke survivors in Singapore, and examine if having a caregiver affected these outcomes. Methods: We retrospectively studied all stroke patients fulfilling inclusion criteria (n=3796) from all Singaporean rehabilitation hospitals from 1996-2005. We used backward mixed model linear regression (multivariate) to test the relationship between independent variables and REs and REy. Admission and discharge Shah-modified Barthel Indices were used to calculate REs and REy. We further explored the effect of caregiver availability, number of caregivers, and relationship of caregiver to patient on REs and REy. Mixed logistic regression identified independent predictors of caregiver availability and caregiver relationship to patient, mixed Poisson modelling identified independent predictors of caregiver number; mixed linear regression identified the relationship of REy and REs with caregiver factors. Results: Having a caregiver independently predicted poorer REs and log REy; other predictors included older age, Malay ethnicity, ischemic stroke, longer time to admission, dementia. Within our population, 95.8% (3640/3796) had caregivers and 94.2% (3429/3640) of them provided physical care (defined as primary caregivers). Of patients with primary caregivers, 41.2% relied on live-in hired help (foreign domestic workers, FDWs), 27.6% on spouses and 21.6% on first-degree relatives. Independent factors associated with caregiver availability and number were older age, female, being married, higher socioeconomic status, and having a religion (all p<0.05). Compared to those who had spouse as primary caregiver, having a child or parent (β=-4.1, 95%CI=-8.0 to -0.1, p=0.042) or FDW (β=-6.9, 95%CI=-9.9 to -3.9, p<0.001) as primary caregiver were predictive of poorer REs, while having a child or parent (β=-0.140, 95%CI=-0.280 to 0.001, p=0.052) or FDW (β=-0.110, 95%CI=-0.210 to -0.001, p=0.048) as primary caregiver were predictive of poorer log REy. Conclusions: In this Asian population, having a caregiver was surprisingly associated with poorer REs and REy in stroke patients, perhaps due to differing socio-cultural contexts; only 49.2% of patients depended on spouse/near relatives as primary caregivers, compared with higher estimates in Western populations. REs further declined with decreasing closeness of relationship between primary caregiver and patient, and REy was poorer in patients with hired help as primary caregivers. Perhaps the role of hired help in stroke caregiving should be re-thought, as dependence on FDWs is high in many Asian cities due to population ageing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".