Evaluating the corporate social responsibility agenda for high-cost novel therapies: roles for government and civil society
Bibliographic record
Abstract
BACKGROUND: Corporate social responsibility (CSR) activity in the pharmaceutical industry is frequently directed towards improving patient access to medicines amongst low-income populations. This research reports on findings from a mixed literature and key informant study of pharmaceutical sector CSR activity and its applicability in the high-cost novel therapeutics space. METHODS: Academic and grey literature documents were extracted from online databases in a rapid literature review, focusing on four key areas of interest: (i) CSR or benefit company activity, (ii) the pharmaceutical industry, (iii) the development and sale of high-cost novel medicines and (iv) the role of government and civil society in this space. Ten semistructured interviews amongst key informants, including medical activists, pharmaceutical industry representatives, patient advocates, employees at nongovernmental organizations (NGOs), consultants for international organizations and academic researchers were also conducted related to these topics. RESULTS: We find that CSR strategies vary depending on partner identity and country ability to pay. Differential pricing schemes and flexible patent approaches tend to be pursued unilaterally by companies, whereas companies frequently partner with local private sector, government, nongovernmental organizations and academic actors when implementing patient support programs, medicines donations, medicines delivery programs and rare and neglected disease research and development (R&D) initiatives. Patient support programs are more prevalent in high-income countries with minimal state-subsidized healthcare, whilst differential and tiered pricing strategies are more frequently pursued in lower-income countries. CONCLUSIONS: Pharmaceutical CSR strategies may benefit from greater coordination with government and civil society actors. Opportunities for government and civil society actors to take an active role in better aligning CSR activity with patient needs and universal health coverage include promoting greater adoption of alternative corporate structures and providing active external recognition of successful CSR initiatives through reputational and funding awards.
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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 | 0.124 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.032 | 0.021 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".