UK cancer healthcare professionals collaborating with colleagues in low- and middle-income counties: Mapping the extent and nature of partnerships.
Bibliographic record
Abstract
e18512 Background: Most cancer deaths occur in low- & middle-income countries (LMICs). In 2020, the UK Global Cancer Network (UKGCN) formed to unite those interested in Global Oncology & to strengthen collaborative work with colleagues in LMICs to reduce morbidity & mortality from cancer. For the first time in the UK, the UKGCN undertook a mapping exercise, to document the number & type of collaborations between the UK & LMIC partners. Methods: A semi-structured survey was developed & performed over 10 weeks from February 2021, to identify UK individuals & institutions engaged in clinical practice, research &/or education with LMIC partners, where the aim was to improve the care of people with/at risk of cancer. The survey was emailed to individuals in NHS hospitals, charities, universities, other organisations, UKGCN members & to contacts identified by a literature search. Results: A total of 639 invitations were sent & 88 responses received. Results demonstrate a range of collaborative efforts spanning many areas of cancer control:health promotion & prevention, diagnosis & treatment to survivorship & palliative care. A wide range of countries were represented: Sub-Saharan Africa, South America, MENA region, China & South-East Asia. Projects included education & training (146), clinical practice (144) & research (226; Table). Funding sources for projects included academic institutions, private sector, United Nations agencies, UK government, arms-length bodies & international government. Conclusions: This mapping exercise has demonstrated considerable UK collaboration with colleagues in LMICs across the continuum, involving all 3 domains of education, practice, & research. This mapping exercise will serve as a baseline on which to build a more accurate database to measure future work. It will enable the UK community to guide domestic strategy, increase efficiency, enable innovation & accelerate collaborations. Information from the survey has been used as a catalyst to create new partnerships between colleagues working in similar geographical settings, encouraging bidirectional learning leading to mutual benefit. Next steps include using this data to support calls for increased funding in global cancer care, & for institutional recognition of, & investment in, Global Oncology as a discipline. The survey will be repeated using more comprehensive methods to increase accuracy with a view to maintaining an up-to-date database.[Table: see text]
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".