A longitudinal study looking at and beyond care recipient health as a predictor of long term care home admission
Bibliographic record
Abstract
BACKGROUND: The unpaid care provided by informal caregivers allows care recipients to live longer in their homes, which often results in fewer unnecessary long term care home (LTCH) admissions. Although the relationship between care recipient's health characteristics and institutionalization is well known, the influence of caregiver distress and caregiving coresidence and relationship on this outcome is less clear. This study examines the association of care recipient care needs, caregiver distress and caregiving coresidence and relationship with care recipient long term care home admission. METHODS: A total of 94,957 resident assessment instruments-home care (RAI-HC), completed between April 01st 2013 and April 01st, 2014 as part of a clinical practice by 14 Local Health Integration Networks (LHINs) in Ontario, Canada, were linked to LTCH admissions within 1 year after completion of the first RAI-HC assessment. Cox models were used to examine whether care recipient health care needs, caregiver distress and caregiving characteristics such as coresidence and relationship were associated with LTCH admission. Age, marital status and gender of the care recipient were included as covariates in the model. RESULTS: Care recipient health care needs and age were the strongest predictors of LTCH admission followed by caregiver distress and caregiving coresidence and relationship. Care recipient marital status was not significant in the survival model. Interestingly, care recipients who were cared for by a coresiding adult child caregiver were less likely to be admitted to a LTCH than care recipients cared for by a spouse caregiver coresiding or not with care recipient. Hazard rates (HR) of admission for care recipients cared for by caregivers coresiding and with other type of relationship with care recipient were not significantly different than HR of care recipients cared for by coresiding child caregivers. CONCLUSIONS: These results emphasize the influence of caregiver distress in LTCH admission and highlight the impact of caregiving relationship and coresidence on this outcome. Policy and decision makers should consider these findings when developing and evaluating interventions aiming to avoid LTCH admissions. Moreover, caregiving coresidence and relationship should be explored in future studies with similar aims, as this information has been neglected in past research.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".