Examining how contexts shape young children’s perspectives of health
Bibliographic record
Abstract
BACKGROUND: We know that the social conditions in which children live exert a strong influence on their health; yet, we do not know how children's experience of these conditions of daily life shape their perspectives of health. METHODS: Through ethnographic research methods, the first author spent 1 year with the 14 6-year-old children involved in this research and examined how the contexts of daily life influenced the children's perspectives of health. The children involved in this study all lived in a neighbourhood characterized as having a complex of mid to high range of neighbourhood factors associated with vulnerability. RESULTS: The findings demonstrate that the children were able to articulate the health requirements of physical activity and healthy eating that supports their health. However, there was a disparity between the children's health knowledge, their perceptions and their contextual realities in relation to health. Children spoke of concerns for their physical safety within their schools and neighbourhoods; their lack of free range of play, and that they had few opportunities to play with or get to know neighbourhood friends. CONCLUSION: Professionals in contact with children and families who live in challenging social conditions need to be aware of how these contexts shape children's understanding of their own health potential.
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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 | 0.003 | 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 | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".