It still takes a village: an epidemiological study of the role of social supports in understanding unexpected health states in young people
Bibliographic record
Abstract
BACKGROUND: This study of adolescent Canadians examines two groups who are anomalous in their health experiences: (1) those with perceived low affluence yet who perceive themselves to have excellent general health status; (2) those of perceived high affluence but who are reporting poor health status. Our hope was to explore the role of social supports in explaining such anomalies. We hypothesized that cumulative levels of social support available to these young people would have an influence on their perceived health status, with more support being associated with better self reported health. METHODS: Young people (n = 26,078 from 436 schools) aged 11-15 years were administered a general health survey in classroom settings during the 2009-10 academic school year. Descriptive and regression-based cross-sectional analyses (with an affluence-social support interaction term) were used to relate both individual and cumulative levels of social support in homes, neighborhoods, schools, and peer groups to self-reported health status. RESULTS: Social supports and their cumulative availability indeed were strongly related to perceived health, with more supports being associated with better self-perceived health. Less affluent children were much more likely to report excellent health in the presence of numerous social supports. More affluent children were much more likely to report poor health in the absence of such supports. The strength and dose-dependent nature of the findings were consistent and striking. CONCLUSIONS: Study findings from this large, contemporary and national analysis affirm the importance of social supports as potential determinants of health for young people from both high and low affluent groups. Conceptually, findings affirm the wisdom of the ancient principle: "it takes a village to raise a child".
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".