Culture, context and community: Ethical considerations for global research
Bibliographic record
Abstract
High-quality research is essential for the generation of scientific nursing knowledge and the achievement of the Millennium Development Goals. However, the incorporation of Western bioethical principles in the study \ndesign may not be suitable, sufficient or relevant to participants in low-income countries and may indeed be \nharmful and disrespectful. Before engaging in global health studies, nurses must consider carefully the cultural \nand social context and values of the proposed setting in order to situate the research within the appropriate ethical framework. \nThe purpose of this paper was to examine the ethical principles and considerations that guide health research conducted in international settings using the example of a qualitative study of Ugandan nurses and \nnurse-midwives by a Canadian researcher. \nThe application of Western bioethical principles with their emphasis on autonomy fails to acknowledge the importance of relevant contextual aspects in the conduct of global research. Because ethics is concerned with how people interact and live together, it is essential that studies conducted across borders be respectful of, \nand congruent with, the values and needs of the community in which it occurs. The use of a communitarian ethical framework will allow nurse scientists to contribute to the elimination of inequities between those who \nenjoy prosperity and good health, and those who do not.
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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 | 0.351 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.081 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.023 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".