The Role of Family Communication in Individual Health Attitudes and Behaviors Concerning Diet and Physical Activity
Bibliographic record
Abstract
This study explored associations among family communication patterns (conversation and conformity orientations), health-specific communication variables, health attitudes, and health behaviors in a sample of 433 family dyads (N = 866). As expected, results of multilevel models revealed that individuals' health attitudes were strongly associated with their self-reported health behaviors. Findings also suggested that perceived confirmation from a family member during health-specific conversations (a) directly influenced health attitudes, (b) partially accounted for the positive relationship between family conversation orientation and health attitudes, and (c) partially accounted for the inverse relationship between family conformity orientation and health attitudes. Similarly, frequency of health-specific communication (a) directly influenced health attitudes, (b) partially accounted for the positive relationship between family conversation orientation and health attitudes, and (c) directly associated with health behaviors. Results from an actor-partner interdependence model (APIM) supported the aforementioned within-person association between a person's own health attitudes and health behaviors, as well as a positive relationship between young adults' health attitudes and their influential family member's health behaviors. Implications of these findings are discussed as they relate to theory and obesity prevention.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".