Contextual factors in value-based decision support to enhance health technologies adoption: the case of biosimilars
Bibliographic record
Abstract
Introduction: Biosimilar medicines play a critical role in enhancing global health outcomes by improving access to effective biologic treatments. However, their acceptance and implementation, particularly in emerging markets, depend not only on clinical evidence but also on the integration of societal, individual, and cultural values. This paper explores how value-based decision-making can support the adoption of biosimilars across diverse contexts. Methods: A multi-stakeholder workshop was conducted with participants from various countries, focusing on decision-making processes for biosimilars in emerging health systems. Discussions addressed stakeholder roles, contextual influences, and the alignment of evidence with values. A Multi-Criteria Decision Analysis (MCDA) framework was proposed as a tool to systematically integrate measurable outcomes and intangible factors such as trust, perceived quality, and cultural acceptance. Results: Key barriers identified included regulatory uncertainties, limited local evidence, regional data protection constraints, and patient preferences for originator biologics. Participants emphasized the importance of adaptable frameworks that reflect local cultural, economic, and systemic conditions. The proposed MCDA approach was viewed as a promising method for capturing complex value dimensions and facilitating transparent, inclusive decision-making. Broader societal benefits of biosimilars, such as economic development through local production, were also highlighted. Discussion: The workshop underscored the need for value-sensitive implementation strategies that go beyond clinical effectiveness. Integrating context-specific values into evidence-based decision-making can foster trust and support the sustainable adoption of biosimilars. The MCDA framework offers a structured approach to operationalize these principles. Future research should test and refine this model in varied health system settings to support its practical application by policymakers, healthcare providers, and industry stakeholders.
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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 | 0.045 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".