“Health for all” and the challenges for pharmaceutical policies: A critical interpretive synthesis over 40 years
Bibliographic record
Abstract
More than 40 years after the Alma-Ata Declaration on Primary Health Care, it is time to take stock. A look back at the evolution of pharmaceutical policies reveals the extent to which international health has transformed in the last four decades. The imperative of equitable access to healthcare, reaffirmed in Astana in 2018, has still not been achieved in many countries across the globe, whereas recent Ebola and COVID epidemics have opened up new political spaces for pharmaceutical development. In response to a gap in the literature with regard to the politics behind global pharmaceutical policymaking, we offer a critical interpretive synthesis of the literature on pharmaceutical policies, in English and French, from 1978 to 2018 inclusively. Our search strategy and inclusion criteria enabled us to select and review 134 papers and books on pharmaceutical policies. Building upon the seminal works of K.S. Rajan, we review the literature under the following assumption: pharmaceutical policies reflect or enact different conceptions of knowledge, political spaces, and value. We then critically discuss our findings in light of the contemporary debates, particularly in the wake of recurring epidemics. We thereby challenge the mainstream perspective according to which pharmaceuticals and pharmaceutical policies must be viewed as value-free, apolitical instruments.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".