Informal Caregiving: Health System Cost Implications and Caregiver Outcomes
Bibliographic record
Abstract
Care provided by unpaid caregivers, is often seen as a lower cost substitute to care provided by health care providers (formal care), and hence the prevailing assumption is that caregivers’ contributions reduce healthcare costs. One unintended consequence of caregiving is a deterioration in caregiver’s health which could a) impact caregivers’ ability to provide care affecting care-recipients’ use of formal healthcare services; and b) result in a potential increase in healthcare utilization for caregivers themselves. Changes in healthcare resource use due to caregiving health consequences are direct costs to the healthcare system which have been largely omitted in healthcare policy and research. The overarching objective of this thesis is to provide a more accurate estimation of costs and consequences associated with caregiving. This is done by investigating patterns of: 1) healthcare use among caregivers; 2) healthcare use among care-recipients due to the consequences of caregiving; and 3) developing and validating a comprehensive tool that could facilitate routine measurement of caregiving and systematic inclusion of caregiving consequences in the policy making process. The first study was a difference-in-differences cohort study examining the impact of caregiving on caregivers’ use of healthcare services, before and after caregiving in comparison to non-caregivers using administrative databases. Results showed that adjusted total healthcare costs for caregivers were 10% lower than non-caregivers two years into caregiving. The second study was a longitudinal study examining the impact of caregiver distress on care-recipient’s use of healthcare services using administrative databases. The study found that caregiver distress was associated with a 4% increase in care-recipient’s total healthcare costs every six months. The third study reported on the development and validation of a caregiving survey. The survey was co-designed with caregivers providing a simultaneous measurement of caregivers’ health, experience of care, and costs. The analysis of the survey revealed four scales relating to care burden, care partner, caregiver assessment, and care integration. This thesis used novel data and methods to estimate the cost and consequences of caregiving and identifies issues that should be considered in healthcare policy making and in the design and evaluation of healthcare services.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".