Measuring and Valuing Informal Care for Economic Evaluation of HIV/AIDS Interventions: Methods and Application in Malawi
Bibliographic record
Abstract
BACKGROUND: Economic evaluation studies often neglect the impact of disease and ill health on the social network of people living with HIV (PLHIV) and the wider community. An important concern relates to informal care requirements which, for some diseases such as HIV/AIDS, can be substantial. OBJECTIVES: To measure and value informal care provided to PLHIV in Malawi. METHODS: A modified diary that divided a day into natural calendar changes was used to measure informal care time. The monetary valuation was undertaken by using four approaches: opportunity cost (official minimum wage used to value caregiving time), modified opportunity cost (caregiver's reservation wage), willingness to pay (amount of money caregiver would pay for care), and willingness to accept (amount of money caregiver would accept for providing care to someone else) approaches. Data were collected from 130 caregivers of PLHIV who were accessing antiretroviral therapy from six facilities in Phalombe district in southeast Malawi. RESULTS: Of the 130 caregivers, 62 (48%) provided informal care in the survey week. On average, caregivers provided care of 8 h/wk. The estimated monetary values of informal care provided per week were US $1.40 (opportunity cost), US $2.41 (modified opportunity cost), US $0.40 (willingness to pay), and US $2.07 (willingness to accept). CONCLUSIONS: Exclusion of informal care commitments may be a notable limitation of many applied economic evaluations. This work demonstrates that inclusion of informal care in economic evaluations in a low-income context is feasible.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".