Assessing the health impact of transnational corporations: its importance and a framework
Bibliographic record
Abstract
BACKGROUND: The adverse health and equity impacts of transnational corporations' (TNCs) practices have become central public health concerns as TNCs increasingly dominate global trade and investment and shape national economies. Despite this, methodologies have been lacking with which to study the health equity impacts of individual corporations and thus to inform actions to mitigate or reverse negative and increase positive impacts. METHODS: This paper reports on a framework designed to conduct corporate health impact assessment (CHIA), developed at a meeting held at the Rockefeller Foundation Bellagio Center in May 2015. RESULTS: On the basis of the deliberations at the meeting it was recommended that the CHIA should be based on ex post assessment and follow the standard HIA steps of screening, scoping, identification, assessment, decision-making and recommendations. A framework to conduct the CHIA was developed and designed to be applied to a TNC's practices internationally, and within countries to enable comparison of practices and health impacts in different settings. The meeting participants proposed that impacts should be assessed according to the TNC's global and national operating context; its organisational structure, political and business practices (including the type, distribution and marketing of its products); and workforce and working conditions, social factors, the environment, consumption patterns, and economic conditions within countries. CONCLUSION: We anticipate that the results of the CHIA will be used by civil society for capacity building and advocacy purposes, by governments to inform regulatory decision-making, and by TNCs to lessen their negative health impacts on health and fulfil commitments made to corporate social responsibility.
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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 | 0.081 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.011 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| 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".