Developing Persuasive Health Messages for a Behavior-Change-Support-System That Promotes Physical Activity
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Bibliographic record
Abstract
This paper describes the first of three experiments conducted to investigate the efficacy of a proposed persuasive mHealth messaging intervention that motivates individuals to become more physically active. In order to develop a set of persuasive health messages that can be used in the principal experiment, which examines a particular message-tailoring strategy, we conducted an online survey through Amazon Mechanical Turk. In this online study participants rated a series of health messages to indicate each message’s level of persuasiveness, as well as the message’s focus. This study was essential, as disagreements exist on how to frame persuasive health messages in the context of promoting physical activity. Among the proposed 57 messages, 14 messages rated as the most persuasive were selected for the principal experiment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it