Bibliographic record
Abstract
The intersections of politics and economics profoundly shape how cancer control is envisioned, prioritised, organised, implemented and experienced in all settings.The political economy of cancer encompasses various elements, such as political decisions, economic systems and societal structures.It interrogates the dynamics that ultimately shape prevalence, prevention, treatment, access and control efforts worldwide, recognising that a broad spectrum of factors -from corporate interests to socioeconomic disparities -can fundamentally shape access to, and use of, prevention and cancer care.Gheorghe provides an overview of how cancer control has been envisioned within the universal health coverage (UHC) agenda.He describes the issues faced by policymakers in addressing the triple challenge of bridging resource gaps, enhancing resource efficiency and targeting resources effectively.He highlights opportunities from leveraging evidence-based plans, pooled procurement and biosimilar utilisation to bridge these gaps.Further, he makes the argument that enhancing cancer control financing will necessitate adherence to UHC financing principles; investment in credible, evidence-informed resource allocation institutions and ongoing political commitment to ensuring equitable access to cancer care.Denburg and Isaranuwatchai explore the global challenge of integrating emerging healthcare innovations, particularly rapid advancements in cancer biotechnologies and new high-cost cancer therapies, into existing health system budgets.The authors highlight the increasing emphasis on value-based care and health technology assessment (HTA) as crucial tools for guiding health system priorities, resource allocations and evidencebased decisions on cancer care funding.They provide an overview of the current landscape of HTA, highlighting its role in supporting valuebased cancer care as well as its use and potential for supporting access amid evolving healthcare innovations.Essue, Chukwu and Aggarwal explore access as a critical policy concern for improving cancer outcomes within health systems, particularly in the pursuit of UHC.The authors highlight and critique common approaches used in the literature for measuring access.They then review how determinants of access intersect with equity for cancer populations and discuss opportunities to foster equitable access to essential, high-quality cancer services.Through this review the authors highlight the challenges and opportunities for ensuring fair access to comprehensive cancer care within evolving healthcare contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".