Socio-cultural integration and holistic health among Indigenous young adults
Bibliographic record
Abstract
BACKGROUND: Research on associations between social integration and wellbeing holds promise to inform policy and practice targets for health promotion. Yet, studies of social connection too frequently rely on overly simplistic measures and give inadequate attention to manifestation and meanings of social integration across diverse groups. We use the term socio-cultural integration to describe expanded assessment of both social and cultural aspects of belonging and connection. METHODS: We examined 7 distinct indicators of socio-cultural integration, identified heterogeneous patterns of responses across these indicators using latent profile analysis, and determined their relevance for wellbeing using survey data from a study with Indigenous communities in the U.S. and Canada. Wellbeing was measured using holistic ratings of self-rated physical, emotional, and spiritual health. RESULTS: Latent profile analysis (LPA) of responses to the 7 socio-cultural integration variables yielded a 3-class model, which we labeled low, moderate, and high integration. Mean scores on self-rated physical, mental and spiritual health were significantly associated with LPA profiles, such that those in the low integration group had the lowest self-rated health scores and those in the high integration group had the highest health scores. With the exception of similar ratings of cultural identification between low and moderate integration profiles, patterns of responses to the diverse socio-cultural integration measures varied significantly across the 3 latent profiles. CONCLUSIONS: Results underscore the importance of expanding our assessment of social integration with attention to the interrelationships of family, community, culture, and our environment. Such concepts align with Indigenous conceptions of wellbeing, and have relevance for health across cultures. More concretely, the indicators of socio-cultural integration used in this study (e.g., cultural identity, having a sense of connectedness to nature or family, giving or receiving social support) represent malleable targets for inclusion in health promotion initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".