HPV Vaccine Access and Cervical Cancer Policymaking Process: A Comparative Governmental Priority Setting Study of Ghana, Rwanda, and Canada
Bibliographic record
Abstract
Cervical cancer is a global health issue that claims over 600,000 lives yearly. While high-income countries (HICs) record lower incidence rates of cervical cancer, the opposite is true for underserved regions, such as sub-Saharan Africa (SSA), where a significant burden of global cervical cancer cases are reported. Over 70% of all cervical cancer is caused by high-risk (strain 16 and 18) human papillomavirus (HPV). Currently, prophylactic vaccines, Gardasil® and Gardasil9®, manufactured by Merck; Cervarix®, manufactured by GlaxoSmithKline (GSK); and Cecolin® manufactured by Xiamen Innovax Biotech Co, prevent HPV-related cervical cancer. However, most low- and middle-income countries (LMICs) national immunization programs do not include these vaccines. The thesis explores governmental priority settings and the policymaking imperatives for nationwide HPV vaccination in Canada, Rwanda, and Ghana. Ghana, a LMIC, has a cancer policy that covers cervical cancer; however, it lacks a specific policy governing the prevention and control of the disease. Rwanda (also a LMIC), on the other hand, was the first country in sub-Saharan Africa to implement a nationwide HPV vaccine program that aims to reduce and eventually eliminate cervical cancer case incidence and mortality. Canada, one of the first Organization for Economic Cooperation and Development (OECD) nations to implement province-wide HPV vaccination programs, provides instructive health policymaking propositions. Primary and secondary data were collected to develop country-specific case studies on the imperatives for the HPV vaccination program/policy in each country. A comparative analysis supports understanding the similarities and dissimilarities in policymaking and the environment within which the HPV vaccine and cervical cancer program were planned, formulated, and organized for implementation as a health intervention instrument. The results show different policy convergence and divergence nodes among the countries studied as governments look for solutions to public problems. Because governments have myriad competing public problems to address, selectively solving some problems and leaving others may depend on priorities and available resources. Prioritizing and deciding to act by implementing public HPV vaccination programs in HICs, such as Canada, and LMICs, such as Rwanda, and not acting due to resource constraints reveals that governments can leverage creative approaches to act on a public problem successfully with or without plentiful resources.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".