Advocacy in HIA: Increasing Our Effectiveness and Relevance as Practitioners to Address Health, Equity, and Democracy
Bibliographic record
Abstract
The role of advocacy in Health Impact Assessment (HIA) is debated among practitioners. Concerns revolve around whether engaging in advocacy undermines objectivity and credibility. While there is agreement that dissemination of findings and recommendations is necessary, there is a spectrum of activities that can be undertaken in an HIA, one end of which might be considered advocacy.In this Perspective from the Field, We posit that in conducting an HIA, practitioners are choosing to advocate for a set of causes that may include improved health, decreased inequity, and increased democracy. We come to the table with these values and the intent to advocate for them. For any HIA to be relevant and effective at advancing these causes in decision-making contexts, practitioners must use the best available evidence and a range of strategies to communicate evidence to policy audiences, including deliberate tactics with community organizations, decision makers, and others that can aid in addressing power imbalances. Though we believe that HIA practice cannot reach its full potential without embracing advocacy, practitioners must make decisions given their context, including local power dynamics as to how far into the advocacy spectrum they venture in any given HIA.This paper is geared towards HIA practitioners and others who want to understand the opportunity advocacy provides. We begin by describing the underlying values of HIA that inspire this perspective, including those in the 1999 Gothenburg Consensus Paper on HIA (Quigley et al., 2006). After briefly describing concerns HIA practitioners may have with advocacy, particularly that it undermines the objectivity and credibility of the HIA process, we then discuss common advocacy activities practitioners might undertake, and ways to address risks these activities may pose. These opportunities to undertake advocacy include partnering with diverse stakeholders, developing advisory committees, gauging the power and policy context, and thinking broadly about the best tactics to effectively communicate findings. We conclude with a case study describing how advocacy was used in the Treatment Instead of Prison HIA in Wisconsin to advance health, equity, and democracy.
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 | 0.329 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.026 | 0.082 |
| Scholarly communication | 0.041 | 0.056 |
| Open science | 0.006 | 0.065 |
| Research integrity | 0.031 | 0.036 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".