Global Health Strategy for Cancer: Think Globally, Act Locally! Building a Collaborative Partnership Between Manitoba (Canada) and Jaffna (Sri Lanka)
Bibliographic record
Abstract
Background and context: The number of people diagnosed with cancer worldwide is estimated to double by 2035. The greatest increase is expected in low- and middle-income countries (LMIC) due to demographic changes, such as ageing and growing populations, and increasing exposure to risk factors. Approximately 8.8 million people die each year of cancer, or one in 6 deaths globally. The Canadian government has recently renewed its commitment as a progressive global citizen with efforts including improvement of global health equity. CancerCare Manitoba is the provincial agency responsible for cancer and blood disorders, including the delivery of a wide range of clinical services from prevention to screening to treatment and supportive services, as well as cancer surveillance, research, and education. CancerCare Manitoba has identified potential partnerships with governments, nongovernmental organizations, academic institutions, and funders to address current and future challenges related to global cancer control. This includes several LMIC partners who have expressed an interest in working with Manitoba on cancer-related issues. In this presentation, we will describe our plans and early experience with a team from the University of Jaffna, the northern region of Sri Lanka. With a focus initially on surveillance and cancer control planning, there is an excellent opportunity for mutual learning and advancement of programs for cancer surveillance and planning. Aim: To establish a local partnership by connecting Manitoba, Canada with an engaged team from the University of Jaffna, Sri Lanka to advance cancer surveillance and planning, and contribute to the “global war on cancer”. Strategy/Tactics: A phased approach is being taken to address locally identified needs for cancer control. CancerCare Manitoba staff will be part of the mentorship team working with local partners in Jaffna to ensure development of local capacity. Specifically, we will: initiate cancer surveillance and establish a cancer registry in Jaffna (building from a cross-sectional study → hospital based registry → regional registry); analyze data and report on patterns; and establish a strategic plan for cancer control. Program/Policy process: Early planning is underway, involving collaborators from Manitoba and Jaffna. A project proposal has been developed to provide scope and acquire seed funding. Outcomes: Success will be determined based on the context of each program, including: establishing a framework for cancer surveillance; satisfaction of local and international partners (e.g., the Global Cancer Surveillance unit at the International Agency for Research in Cancer); and production of reports as a basis for cancer control. What was learned: Early learnings include the importance of local engagement and dedicated mentorship to advance global health equity, manage challenges around (sustained) funding, and establish a foundation of motivated partners.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".