Caregivers’ willingness-to-pay for Alzheimer’s disease medications in Canada
Bibliographic record
Abstract
We studied caregivers' willingness-to-pay for Alzheimer's disease drug therapy. We recruited 216 caregivers of persons with mild or moderate Alzheimer's disease and presented them with four scenarios describing a hypothetical Alzheimer's disease medication. The scenarios described the medication as capable of either treating the symptoms of disease or modifying the course of disease. The scenarios also presented two different probabilities of adverse effects occurrence, i.e., 0% or 30%. Most caregivers said they would pay out-of-pocket for the medication, with support for such payment ranging from 68% to 93%, depending on the specific scenario. The highest level of support was for the 'disease modifying and no adverse effects' scenario, while the lowest level was for the 'symptom treatment and 30% chance of adverse effects' scenario. On average, caregivers' monthly willingness-to-pay out-of-pocket for the medication ranged from $214 to $277 (Canadian dollars). Dollar amounts were highest for the 'disease modifying and no adverse effects' scenario and lowest for the 'symptom treatment and 30% chance of adverse effects' scenario. Support for out-of-pocket payment and specific dollar amounts were highest when the medication did not involve adverse effects. Caregivers placed more value on the absence of adverse effects than on drug efficacy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".