Making “ethical safe space” in the translation of contested knowledge: The role of community debate in defining end-of-life decision ethics
Bibliographic record
Abstract
OBJECTIVE: The objectives of this article are, first, to document a unique process of research knowledge translation (KT), which the authors describe as the creation of "ethical safe space," and, second, to document the narratives of forum participants and describe their interaction in a dialogue about vulnerability, the authority of physicians, and the perspective of people with disabilities on the policy. METHOD: Narrative data from qualitative interviews with individual key informants and focus groups were used to identify speakers with specific expertise on policy, disability perspectives, and bioethical issues, who were invited to participate in the Forum on Ethical Safe Space. The planning workgroup adopted a model for enabling representative participation in the public forum designed to reduce the impact of physical, sensory, financial, language, and professional status barriers. Using the transcripts and keynote speakers' printed texts, primary themes and patterns of interaction were identified reflecting the alternative perspectives. Through the development of a workshop on ethical, legal, and disability-related implications of professional policy guidelines developed by the College of Physicians and Surgeons of Manitoba, we provided a qualitative analysis of the discourse involving experts and disability community members supporting alternative positions on the impact of the policy statement, and discuss ethical, legal, and disability rights issues identified in the public debate. RESULTS: Contested policy and ethical frameworks for making decisions about withdrawing and withholding life supporting treatment may influence both the perspectives of palliative care providers and patients referred to palliative care facilities. An innovative model for KT using a public forum that enabled stakeholders with conflicting perspectives to engage with ethical and professional policy issues asserting the physician's authority in contested decisions involving withdrawing or withholding life-supporting treatment, was a successful way to engage stakeholders supporting alternative positions on the impact of the policy statement and to discuss ethical, legal, and disability rights issues identified in the public debate. SIGNIFICANCE OF RESULTS: Discussion during the forum revealed several benefits of creating ethical safe space. This model of workshop allows space for participation of stakeholders, who might not otherwise be able to interact in the same forum, to articulate their perspectives and debate with other presenters and audience members. Participants at the forum spoke of the creation of ethical safe space as a starting point for more dialogue on the issues raised by the policy statement. The forum was, therefore, seen as a potential starting point for building conversation that would facilitate revising the policy with broader consultation on its legal and ethical validity.
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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 | 0.288 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.027 | 0.162 |
| Scholarly communication | 0.041 | 0.045 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".