Effects of Social Network Exposure on Nutritional Learning: Development of an Online Educational Platform
Bibliographic record
Abstract
BACKGROUND: Social networking sites (SNSs) such as Facebook have the potential to enhance online public health interventions, in part, as they provide social exposure and reinforcement. OBJECTIVE: The objective of the study was to evaluate whether social exposure provided by SNSs enhances the effects of online public health interventions. METHODS: As a sample intervention, we developed Food Hero, an online platform for nutritional education in which players feed a virtual character according to their own nutritional needs and complete a set of virtual sport challenges. The platform was developed in 2 versions: a "private version" in which a user can see only his or her own score, and a "social version" in which a user can see other players' scores, including preexisting Facebook friends. We assessed changes in participants' nutritional knowledge using 4 quiz scores and 3 menu-assembly scores. Monitoring feeding and exercising attempts assessed engagement with the platform. RESULTS: The 2 versions of the platform were randomly assigned between a study group (30 members receiving the social version) and a control group (33 members, private version). The study group's performance on the quizzes gradually increased over time, relative to that of the control group, becoming significantly higher by the fourth quiz (P=.02). Furthermore, the study group's menu-assembly scores improved over time compared to the first score, whereas the control group's performance deteriorated. Study group members spent an average of 3:40 minutes assembling each menu compared to 2:50 minutes in the control group, and performed an average of 1.58 daily sport challenges, compared to 1.21 in the control group (P=.03). CONCLUSIONS: This work focused on isolating the SNSs' social effects in order to help guide future online interventions. Our results indicate that the social exposure provided by SNSs is associated with increased engagement and learning in an online nutritional educational platform.
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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 | 0.001 | 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".