Should Strengthening Bonds Be a Public Health Priority?
Bibliographic record
Abstract
Abstract. Background: Previous research demonstrates the importance of close relationships on our physical health. However, to what extent the quality of our social relationships impacts our health, relative to other important health behaviors (e.g., smoking, drinking alcohol, and physical exercise), is less clear. Aims: Our goal was to use a nationally representative sample of Canadian adults to assess the relative importance of the quality of one’s social relationships (close emotional bonds and negative social interactions), relative to important health behaviors on physical health outcomes previously linked to social relationship quality. Method: Data ( N = 25,113) came from the Canadian Community Health Survey in 2012, a cross-sectional survey administered by Statistics Canada (2013) . The predictor variables were the presence of close emotional bonds, negative social relationships, type of smoker, type of drinker, and weekly hours of physical activity. The outcome variables were a current or previous diagnosis of high blood pressure, cancer, stroke, reports of current illness or injury, pain, and self-reported physical health. Results: Using regressions, we found that negative social interactions were more important than other health behaviors in relation to current injury/illness and pain. Physical activity was most strongly related to self-rated health, followed by negative social interactions and then close emotional bonds. Alcohol consumption was more related to the prevalence of stroke. Conclusions: Our findings suggest that negative social interactions may be more related to acute or minor physical health conditions, but social relationships may not be more strongly related to more chronic, life-threatening health conditions than other health behaviors.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".