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Record W2901122674 · doi:10.1111/nup.12232

Bearing witness in nursing practice: More than a moral obligation?

2018· article· en· W2901122674 on OpenAlexaffabout
Mikelle Djkowich, Christine Ceci, Olga Petrovskaya

Bibliographic record

VenueNursing Philosophy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWitnessInjusticeObligationCommissionMoral obligationSociologyPoliticsPrivilege (computing)LawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.123
GPT teacher head0.531
Teacher spread0.408 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2018
Admission routes2
Has abstractyes

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