A Gradient in Education Due to Health? Evidence from the Study of Health Behavior in School-Aged Children
Bibliographic record
Abstract
Research exploring the relationship between education and health suggests that people with higher levels of schooling report better health. To emphasize health as a determinant of educational achievement, this article establishes a gradient in education by health among Canadian students. Using data from the 2006 Health Behaviour in School-aged Children (HBSC) study, the relationship between self-rated health and achievement is examined for 8,626 students from 131 schools. The variation of the gradient in education by health within and between schools suggests that increases in self-rated health are associated with increased achievement for students. Moreover, the within-school regression accounted for 2.7 % of the variation in achievement due to health, whereas the between-school regression slope accounted for 19.8% of the variation in achievement due to health. Inequalities in achievement associated with health were more pronounced between schools than within schools. Policy implications as they relate to the findings are discussed. La recherche portant sur le rapport entre l’éducation et la santé donne à penser que les gens les plus instruits se disent en meilleure santé. Afin de mettre en relief la santé comme facteur déterminant dans le niveau d’instruction, cet article développe une échelle liant le niveau de scolarité et la santé chez les élèves canadiens. Puisant dans des données de l’enquête sur les comportements liés à la santé chez les enfants d’âge scolaire (2006), nous examinons le rapport entre la santé et la scolarité telles que décrites par 8 626 élèves provenant de 131 écoles. La variation notée dans le rapport scolarité/santé à l’intérieur des écoles et entre elles donne à penser que plus l’état de santé déclaré est positif, plus le rendement est élevé chez les élèves. De plus, la régression au sein des écoles représente 2,7% de la variation dans le rendement attribuable à la santé, alors que la régression entre les écoles représente 19,8 % de la variation dans le rendement attribuable à la santé. Les inégalités dans le rendement associé à la santé étaient plus prononcées entre les écoles qu’au sein des écoles. Nous discutons des incidences sur la politique qui découlent de ces résultats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".