Canadian Centre for Health Economics
Bibliographic record
Abstract
To Vaccinate or to Procrastinate? That is the Prevention Question⇤ Invoking Yaari’s dual theory we develop a model of individual vaccination decisions that incorpo-rates quasi-hyperbolic discounting (present-biasedness), risk aversion, and information. We test the resulting hypotheses for the flu season 2010/2011 using a representative German data set. It turns out that quasi-hyperbolic discounting men vaccinate with a significantly lower probability than ex-ponential discounters; they tend to procrastinate. There is no such delay in the prevention behavior of women who tend to vaccinate despite their distorted time preference. Risk aversion is positively related to the probability to vaccinate for men, while the association is negative for women. Well informed individuals have a much higher propensity to vaccinate than poorly informed individuals. Our results suggest that public health policy should not only concentrate on providing information about the flu and the flu shot but also increase the awareness that distorted time preferences may have a bearing on individual prevention decisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".