Review of Education‐Focused Health Impact Assessments Conducted in the United States
Bibliographic record
Abstract
BACKGROUND: Health impact assessment (HIA) provides a structured process for examining the potential health impacts of proposed policies, plans, programs, and projects. This study systematically reviewed HIAs conducted in the United States on prekindergarten, primary, and secondary education-focused decisions. METHODS: Relevant HIA reports were identified from web sources in late 2015. Key data elements were abstracted from each report. Four case studies were selected to highlight diversity of topics, methods, and impacts of the assessment process. RESULTS: Twenty HIAs completed in 2003-2015 from 8 states on issues related to prekindergarten through secondary education were identified. The types of decisions examined included school structure and funding, transportation to and from school, physical modifications to school facilities, in-school physical activity and nutrition, and school discipline and climate. Assessments employed a range of methods to characterize the nature, magnitude, and severity of potential health impacts. Assessments fostered stakeholder engagement and provided health-promoting recommendations, some of which were subsequently incorporated into school policies. CONCLUSIONS: Health impact assessment is a promising tool that education, health, and other stakeholders can use to maximize the health and well-being of students, families, and communities.
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".