Bearing witness in nursing practice: More than a moral obligation?
Bibliographic record
Abstract
In this paper, we explore the concept of bearing witness in nursing practice. We examine the description of bearing witness in the nursing literature, particularly that offered by William Cody who suggests that bearing witness results in the limited moral obligation of "true presence." We then turn to Lorraine Code's work on testimony, drawing parallels between the concepts of testimony and bearing witness. Code suggests that receiving testimony results in a responsibility to respond, and that this is an ethico-political obligation. We discuss these ideas in relation to a Canadian exemplar of witnessing the Truth and Reconciliation Commission of Canada's work to understand and address the historical injustices done to Indigenous peoples in Canada. Here, we focus on the Commission's definition of witnessing and highlight the experience of Shelagh Rogers who served as an honorary witness. As an outcome of our analysis, we suggest that bearing witness in nursing practice is most usefully conceptualized as both a moral and a political obligation. Implications for nursing practice are suggested, including first, the need to critically examine our own understandings of power and privilege in order to authentically bear witness and avoid being complicit in injustice, and second, the concomitant responsibility to take action to challenge injustice once we have borne witness to it.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.068 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".