Social Support for Changing Multiple Behaviors: Factors Associated With Seeking Support and the Impact of Offered Support
Bibliographic record
Abstract
INTRODUCTION: Social support is important for behavior change, and it may be particularly important for the complexities of changing multiple risk behaviors (MRB). Research is needed to determine if participants in an MRB intervention can be encouraged to activate their social network to aid their change efforts. METHODS: Healthy Directions 2, a cluster-randomized controlled trial of an intervention conducted in two urban health centers, targeted five behaviors (physical activity, fruit and vegetable intake, red meat consumption, multivitamin use, and smoking). The self-guided intervention emphasized changing MRB simultaneously, focused on self-monitoring and action planning, and encouraged participants to seek support from social network members. An MRB score was calculated for each participant, with one point being assigned for each behavioral recommendation that was not met. Analyses were conducted to identify demographic and social contextual factors (e.g., interpersonal, neighborhood, and organizational resources) associated with seeking support and to determine if type and frequency of offered support were associated with changes in MRB score. RESULTS: Half (49.6%) of participants identified a support person. Interpersonal resources were the only contextual factor that predicted engagement of a support person. Compared to individuals who did not seek support, those who identified one support person had 61% greater reduction in MRB score, and participants identifying multiple support persons had 100% greater reduction. CONCLUSION: Engagement of one's social network leads to significantly greater change across multiple risk behaviors. Future research should explore strategies to address support need for individuals with limited interpersonal resources.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".