Bibliographic record
Abstract
Willingness to pay is a term used in economics, which can be defined as the maximum amount a person would be prepared to pay, sacrifice or exchange in order to receive goods or services or to avoid something that is undesired. It can be used in medicine as a method for assessing the value of health benefits in a cost-benefit analysis. One indication of the importance of this concept is the progressive appearance of this term in the National Institutes of Health’s PubMed database. The first appearance was in 1972, but it then remained uncommon, with less than 10 mentions per year until the 1990s. From 2000 to 2010, the numbers of appearances of this term grew from 69 times a year to 213 times a year. In 2013, this term appeared 355 times in the PubMed database. The greatest usages of willingness to pay within medicine are in Pharmacoeconomics and Health Economics. Nonetheless, this term can be a valuable addition within any field in which cost-benefit analysis is desired. Examples of this usage in 2013, retrieved from PubMed, include: a) Patients’ willingness to pay for Alzheimer’s disease medication in Canada.1 b) Parent preference in Switzerland for easy-to-use attributes of growth hormone injection devices quantified according to willingness to pay.2 c) Willingness to pay for anterior cruciate ligament reconstruction.3
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.012 |
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; both teacher heads agree on what is shown here.
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".