Do age, psychosocial, and health characteristics alter the weak and strong tie composition of network diversity and core network size in urban adults?
Bibliographic record
Abstract
Social capital and social support are two key constructs in the study of social networks and health. Despite their importance, little research has sought to examine the characteristics of those social ties by which individuals access social capital and support resources. Network diversity - a key structural feature in accessing social capital - refers to a person's broad but generally weak and heterogeneous social ties; core network size - a key structural feature in accessing social support - refers to the close, strong ties in personal networks. Our study examines whether the tie strength composing network diversity and core network size varies according to age-, psychosocial-, or health-related characteristics. Data came from the Montreal Neighbourhood Networks and Healthy Aging (MoNNET-HA) study, a representative sample of 2707 Montreal, Canada adults. Position and name generators were used to collect data on network diversity and core networks, and whether access to social resources was through kin, friends, or acquaintances. Multilevel negative binomial regression was used to account for the counts of different tie strengths nested within individuals and tracts. Network diversity and core network size both declined with older age groups, with those declines being more noticeable in not having ties at all or fewer ties with friends. Psychosocial and health factors altered the relative contribution of kin, friends and acquaintances to network diversity and core network size in similar patterns. Understanding the tie composition of network diversity and core network size can contribute to our knowledge of the social mechanisms linking social capital and support to health outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".