Leveraging goals to incentivize healthful behaviors across adulthood.
Bibliographic record
Abstract
= 450). Participants were randomly assigned to 1 of 5 conditions: personal, loved one, charity, choice, or a no-incentive control group. Average daily step counts were measured using pedometers during a baseline week, during the incentivized period, and after the incentivized period ended. Overall, financial incentives significantly increased walking compared to a control group. Whereas personal incentives were effective regardless of age, incentives to earn for charities were starkly more effective in older adults than younger adults. Moreover, 1 week after the incentivized period ended, older participants were more likely to maintain increased step counts, whereas younger people reverted to baseline step counts. Findings suggest that financial incentives can increase walking in a wide age range and that charitable incentives may be especially effective in health interventions targeting older adults. The importance of aligning incentives with age-related goals is discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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".