Valuing care recipient and family caregiver time: A comparison of methods
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study is to compare the approaches used for valuing family caregiver and care recipient time devoted to providing and receiving care. METHODS: Valuation approaches were operationalized within a cohort of cystic fibrosis care recipients (n = 110). Base-case analyses, grounded in human capital theory, applied earnings estimates to caregiving time to impute the market value of time lost from labor. Unpaid labor and leisure time was valued with a replacement cost (homemaker's wage rate). Total time costs were computed and sensitivity analyses were conducted to describe the effects of alternative valuation methods on total costs. RESULTS: The mean time cost per care recipient-caregiver dyad over 28 days was $2,026CAD. The majority (76 percent) of time costs were due to losses from unpaid labor and leisure time. Varying the valuation of paid labor time did not result in significantly different total time costs (p = .0877). However, varying the method of valuing unpaid labor and leisure time did significantly affect total costs (p < .0001). CONCLUSIONS: Care recipients and caregivers primarily lost time from unpaid labor and leisure in the treatment of cystic fibrosis. Moreover, when the above losses were aggregated, the method of valuation greatly influenced overall results. The findings clearly indicate that omitting caregiver and unpaid labor and leisure costs may result in an inaccurate assessment of ambulatory and home-based healthcare programs.
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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 | 0.042 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".