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Perils of proximity: a spatiotemporal analysis of moral distress and moral ambiguity

2004· review· en· W1998892732 on OpenAlexaff
Elizabeth Peter, Joan Liaschenko

Bibliographic record

VenueNursing Inquiry · 2004
Typereview
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAmbiguityClosenessPsychologyRelation (database)Moral disengagementSocial psychologyEpistemologyNursingSociologyMedicinePhilosophyComputer science

Abstract

fetched live from OpenAlex

The physical nearness, or proximity, inherent in the nurse-patient relationship has been central in the discipline as definitive of the nature of nursing and its moral ideals. Clearly, this nearness is in service to those in need of care. This proximity, however, is not unproblematic because it contributes to two of the most prolonged difficulties, both for individual nurses and the discipline of nursing--moral distress and moral ambiguity. In this paper we explore proximity using both a moral and geographical lens and offer some insights regarding this practice reality. We examine the effect of proximity to patients on nurses' moral responsiveness, particularly as it affects nurses' moral distress. Proximity is paradoxical in this regard because, while it propels nurses to act, it can also propel nurses to ignore or abandon. Likewise, we argue that nursing's tendency to define itself in relation to the closeness of the nurse-patient relationship leads to problems of moral ambiguity. Our recommendations include moving others closer to the bedside and thus to the work of nursing in the literal and theoretical sense.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.010
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.337
GPT teacher head0.562
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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

Citations201
Published2004
Admission routes1
Has abstractyes

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