Schering Plough Lecture 2009: The twinning experience: Meaning of an educational program for nurses in Kenya
Bibliographic record
Abstract
The International Society of Nurses in Cancer Care (ISNCC) has, as part of its mission, the goal of fostering the growth of oncology nursing in parts of the world where cancer nursing is not perceived as a specialty. The idea of “twinning” high-resource countries with middle/low-resource countries was proposed, as a potential strategy to the ISNCC Board for achieving this goal. The Odette Cancer Centre Nursing Division from Sunnybrook Health Sciences Centre stepped forward and indicated it would be a “test case” for this new twinning or partnership program. Based on several conversations between M. Fitch when she was President of ISNCC and D. Makumi, Aga Khan University Hospital in Nairobi, Kenya, an agreement was reached to work together. A proposal was then developed for submission to the International Union Against Cancer (UICC) to hold an oncology nursing workshop on chemotherapy administration in Nairobi. This was a first step in our twinning experience. This article describes the experiences of the four nurses from Odette Cancer Centre (who prepared and offered the program) and the nurses in Kenya (who helped to plan and participated in the program) in working collaboratively as “twins.”
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".