Prioritizing Health: A Systematic Approach to Scoping Determinants in Health Impact Assessment
Bibliographic record
Abstract
The determinants of health are those factors that have the potential to affect health, either positively or negatively, and include a range of personal, social, economic, and environmental factors. In the practice of health impact assessment (HIA), the stage at which the determinants of health are considered for inclusion is during the scoping step. The scoping step is intended to identify how the HIA will be carried out and to set the boundaries (e.g., temporal and geographical) for the assessment. There are several factors that can help to inform the scoping process, many of which are considered in existing HIA tools and guidance; however, a systematic method of prioritizing determinants was found to be lacking. In order to analyze existing HIA scoping tools that are available, a systematic literature review was conducted, including both primary and gray literature. A total of 10 HIA scoping tools met the inclusion/exclusion criteria and were carried forward for comparative analysis. The analysis focused on minimum elements and practice standards of HIA scoping that have been established in the field. The analysis determined that existing approaches lack a clear, systematic method of prioritization of health determinants for inclusion in HIA. This finding led to the development of a Systematic HIA Scoping tool that addressed this gap. The decision matrix tool uses factors, such as impact, public concern, and data availability, to prioritize health determinants. Additionally, the tool allows for identification of data gaps and provides a transparent method for budget allocation and assessment planning. In order to increase efficiency and improve utility, the tool was programed into Microsoft Excel. Future work in the area of HIA methodology development is vital to the ongoing success of the practice and utilization of HIA as a reliable decision-making tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".