Where Health, Planning, and Community Empowerment Meet: A Rapid Health Impact Assessment Model and its Application in Los Angeles
Bibliographic record
Abstract
There has been a surge of interest in Health Impact Assessment (HIA) in the United States, contributing to a range of practices that vary in their effort, duration, and complexity. HIA is a systematic but flexible process used to increase discussion of impacts to human health in decisions, such as in planning, which traditionally would not consider mental, social, or physical health and well-being but can affect them. Stakeholder partici- pation is a core element of HIA practice, yet research suggests a gap between the intention of including meaningful participation and its implementation. This is particularly true in what are known as rapid HIAs due to their especially short timelines and the resource-in- tensiveness of meaningful community participation. We sought to address that gap, draw- ing on standard HIA practice and a Consensus Conference approach from Denmark to develop a rapid Health Impact Assessment model that includes meaningful participation and fosters empowerment among impacted residents using limited resources and within a short decision-making timeline. This paper describes a 2012 piloting of the rapid HIA model on a proposed stadium development project and findings about the HIA’s impact, based on interviews with project stakeholders and a review of project outcomes. Findings indicated that the new model was successful: it contributed to a broader strategy that won a variety of health benefits and measures for the community; residents were engaged and felt empowered by the process; the rapid HIA helped organizations meet their goals; and the project contributed to changes in the stadium proposal that benefit health. The findings suggest that the model helps address a potential conflict practitioners and planners face between conducting a project with a short timeline and more fully engaging community stakeholders in the process.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".