Study protocol: Social Health Impact of Network Effects (SHINE) Study
Bibliographic record
Abstract
Humans are a fundamentally social species whose well-being depends on how we connect with and relate to one another. As such, scientific understanding of factors that promote health and well-being requires insight into causal factors present at multiple levels of analysis, ranging from brain networks that dynamically reconfigure across situations to social networks that allow behaviors to spread from person to person. The Social Health Impacts of Network Effects (SHINE) study takes a multilevel approach to investigate how interactions between the mind, brain, and community give rise to well-being. The SHINE protocol assesses multiple health and psychological variables, with particular emphasis on alcohol use, how alcohol-related behavior can be modified via self-regulation, and how thoughts, feelings, and behaviors unfold in the context of social networks. An overarching aim is to derive generalizable principles about relationships that promote well-being by applying multilayer mathematical models and explanatory approaches such as network control theory. The SHINE study includes data from 711 college students recruited from social groups at two universities in the northeastern United States of America, prior to and during the COVID-19 pandemic. Participants completed at least one of the following study components: baseline self-reported questionnaires and social network characterization, self-regulation intervention assignment (mindful attention or perspective taking), functional and structural neuroimaging, ecological momentary assessment, and longitudinal follow-ups including questionnaires and social network characterization. The SHINE dataset enables integration across modalities, levels of analysis, and timescales to understand young adults’ well-being and health-related decision making. Our goal is to further our understanding of how individuals can change their thoughts, feelings, and behaviors, and of how these changes unfold in the context of social networks.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.152 | 0.031 |
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".