The Value of Mainstreaming Human Rights into Health Impact Assessment
Bibliographic record
Abstract
Health impact assessment (HIA) is increasingly being used to predict the health and social impacts of domestic and global laws, policies and programs. In a comprehensive review of HIA practice in 2012, the authors indicated that, given the diverse range of HIA practice, there is an immediate need to reconsider the governing values and standards for HIA implementation [1]. This article responds to this call for governing values and standards for HIA. It proposes that international human rights standards be integrated into HIA to provide a universal value system backed up by international and domestic laws and mechanisms of accountability. The idea of mainstreaming human rights into HIA is illustrated with the example of impact assessments that have been carried out to predict the potential effects of intellectual property rights in international trade agreements on the availability and affordability of medicines. The article concludes by recommending international human rights standards as a legal and ethical framework for HIA that will enhance the universal values of nondiscrimination, participation, transparency and accountability and bring legitimacy and coherence to HIA practice as well.
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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 | 0.214 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.111 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".