On Supplementing “Foot in the Door” Incentives for eHealth Program Engagement
Bibliographic record
Abstract
Financial health incentives, such as paying people to lose weight, are being widely implemented by Western nations and large corporations. A growing number of studies have tested the impact of incentives on health behaviors, though few have evaluated the approach on a population-scale. In this issue of the Journal of Medical Internet Research, Liu et al add to the evidence-base by examining whether a single incentive can motivate enrollment and engagement in a preventive eHealth program in a sample of 142,726 Canadian adults. While the incentives increased enrollment significantly (by a factor of about 28), a very high level of program attrition was noted (90%). The "foot in the door" incentive technique employed was insufficient; enrollees received incentives for signing-up for, but not for engaging with, the eHealth program. To supplement this technique and drive sustained behavior change, several theoretically- and empirically-based strategies are proposed. Specifically, incentives indexed to behavioral achievements over time are highlighted as one approach to boost engagement in this population in the future.
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 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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.031 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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".