Developing a School Asthma Policy
Bibliographic record
Abstract
Schools are faced with the challenging mandate of addressing the learning needs of students while simultaneously managing a gamut of behavior and health problems. School health policies have been successfully used for many health-related issues. Although asthma is the most common chronic disease among children, schools receive only a small amount of support to manage asthma issues. This article describes our experiences in developing an asthma policy in schools. The problem of asthma in schools was assessed as part of a comprehensive community-based asthma intervention, and a plan was established to develop a school asthma policy. The goal of the policy was to facilitate a physical and social environment that enabled students with asthma to control optimally their condition, have a good quality of life, and learn effectively. Specific objectives included enhancing environmental control, educating staff, clarifying medication protocols, and responding appropriately to symptoms. Strategies in developing the policy included strengthening collaboration and networks among health, school, and community sectors; defining the role of the school and health sectors; designing the policy; refining the policy based on stakeholder feedback; and developing an evaluation plan.
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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".