Tackling Pharmaceutical Pollution Along the Product Lifecycle: Roles and Responsibilities for Producers, Regulators and Prescribers
Bibliographic record
Abstract
Pharmaceuticals produce considerable environmental harm. The industry's resource-intensive nature, coupled with high energy costs for manufacturing and transportation, contribute to the "upstream" harms from greenhouse gas emissions and ecosystem pollution, while factors such as overprescription, overuse, and pharmaceutical waste contribute to the "downstream" harms. Effectively addressing pharmaceutical pollution requires an understanding of the key roles and responsibilities along the product lifecycle. In this commentary, we argue that three actors-producers, regulators, and prescribers-have unique and interdependent responsibilities to address these issues. Producers and market access regulators are upstream actors who can manage and mitigate harms by both shifting manufacturing, business practices, and regulatory requirements and producing transparent, robust data on environmental harms. By contrast, prescribers are downstream actors whose capacity to reduce environmental harms arises principally as a "co-benefit" of reducing inappropriate prescribing and overuse. Potentially complicating the prescriber's role are the calls for prescribers to recommend "environmentally preferable medicines". These calls continue to increase, even with the sparsity of transparent and robust data on the impact of pharmaceuticals on the environment. Recognizing the interdependencies among actors, we argue that, rather than being ineffectual, these calls draw needed attention to the critical responsibility for upstream actors to prioritize data production, reporting standards and public transparency to facilitate future downstream efforts to tackle pharmaceutical pollution.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".