An Update From Your Public Health Nutritionist
Bibliographic record
Abstract
A child’s environment whether it be where they learn, live, or play, has a strong influence on their health and learning outcomes. Health and education are co-dependent: healthy students are better learners and better educated people are healthier.1 A variety of factors impact a person’s health in an environment; these are often referred to as the Social Determinants of Health (SDoH).2 Some SDoH include education, early childhood development, food insecurity, housing, income, employment, as well as, social exclusion and safety nets.2 Majority of children and youth spend the bulk of their day in a school environment. By supporting healthy school environments, we not only support better health outcomes for our children as they grow, but we can also improve learning outcomes. The Pan-Canadian Joint Consortium for School Health (JCSH) has developed a FREE, online tool called the Healthy School Planner. Schools can use this tool to assess their school environment. Once the assessment is completed, tailored feedback is provided along with a list of resources that help you to take action. Data can be saved and tracked over time. Gather a representative school team and get started today!
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.101 | 0.078 |
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".