Individual time preferences and obesity
Bibliographic record
Abstract
Purpose Behavioral economic analysis of health-related behavior is a potentially useful approach to study and control non-communicable diseases. The purpose of this paper is to explore the time preferences of individuals and its impact on obesity in an adult population of Iran. Design/methodology/approach A structured questionnaire was completed by 792 individuals who were randomly selected from the participants of an ongoing national Prospective Epidemiological Research Studies in IrAN cohort study in West of Iran. The quasi-hyperbolic discounting model was used to estimate the parameters of time preferences and a probit regression model was used to explore the correlation between obesity and time preferences. Findings There was a statistically significant correlation between obesity and both the long-run patience and present-biased preferences of participants. Individuals with a low level of long-run patience were 10.2 percentage points more likely to be obese compared to individuals with a high level of long-run patience. The probability of being obese increased by 11 percentage points in present-biased individuals compared to future biased individuals. Originality/value The long-run patience and time inconsistent preferences were significant determinants of obesity. Considering the time-inconsistent preferences in the development of policies to change obesity-related behavior among adults might increase the success rate of the interventions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".