Family networks and health among Métis aged 45 or older.
Bibliographic record
Abstract
BACKGROUND: Social networks are important for promoting and maintaining health and well-being. Social networks, including family and friendship ties, are sources of emotional, practical and other support that enhance social participation and help combat isolation and loneliness. Aboriginal seniors have been identified as a population at risk of social isolation. DATA AND METHODS: The data are from the 2012 Aboriginal Peoples Survey, a national survey of First Nations people living off reserve, Métis, and Inuit. Frequencies, cross-tabulations, and logistic regression models were used to look at family networks and self-perceived general and mental health among Métis aged 45 or older. RESULTS: An estimated 48% of Métis men and 60% of Métis women aged 45 or older had strong family networks. Older age, lower education, and non-participation in the labour force were associated with strong networks. Métis men and women with strong family networks had higher odds than did those with weak networks of reporting positive mental health, even when potential confounders were taken into account. Among Métis men, a relationship between strong family networks and positive general health was also observed. DISCUSSION: Strong family networks are associated with positive self-perceived general and mental health among Métis adults. In addition to individual behaviours, family well-being is important for general health promotion.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".