A "Nudge" at All? The Jury Is Still Out on Financial Health Incentives
Bibliographic record
Abstract
A comprehensive, multi-level approach to curb chronic disease-related costs in Canada is needed. One target for intervention is the economic domain. The emergence of user financial incentives (UFI) in public health policy as well as their broad implementation in corporate settings has stimulated a growing but limited body of research in this area. The authors'position is that the jury is still out on the question of their effectiveness in sustaining long-term health behaviour change, given the nature of the UFI that have been designed and delivered to date--that is, UFI with limited theoretical and contextual consideration. It is their contention that manipulating UFI design features (there are seven core features with a range of attributes) to exploit contextual (e.g., personal income) and theoretical (e.g., self-efficacy) factors may optimize UFI effectiveness over the long term. Although UFI are not the solution, they might very well be apart.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.066 | 0.071 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".