Incorporating mental health into health impact assessment in the United States: a systematic review
Bibliographic record
Abstract
Purpose Health impact assessment (HIA) is a systematic research and public engagement tool used to elevate health and equity in public policies. However, HIA practitioners often overlook potential mental health impacts. The purpose of this paper is to review the degree to which mental health is included in HIAs in the USA. Design/methodology/approach The authors conducted a systematic review of 156 HIAs that were completed between 1993 and 2013 for their inclusion of mental health. HIAs were subdivided to assess if mental health conditions or their determinants were measured, and if predictions or mitigation strategies were made in the scoping, assessment, or recommendations phases. Findings Overall, 73.1 percent of HIAs included mental health. Of the HIAs that included mental health (n=114), 85.1 percent also included the determinants of mental health and 67.6 percent included mental health outcomes. 37.7 percent of HIAs measured baseline mental health conditions and 64.0 percent predicted changes in mental health as the result of implementing the proposed policy, plan, or program. Among the HIAs that made predictions about mental health, 79.5 percent included recommendations for potential changes in mental health, while only 46.6 percent had measured mental health at baseline. Research limitations/implications Although many HIAs included mental health in some capacity, this paper quantifies that mental health is not included in a robust way in HIAs in the USA. This presents a difficulty for efforts to address the growing issues of mental health and mental health inequities in the populations. Originality/value This paper represents the first academic endeavor to systematically assess the state of the field of HIA for its inclusion of mental health.
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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 | 0.030 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".