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Record W2167662493 · doi:10.1177/0969733011401121

Ethical conflicts with hospitals: The perspective of nurses and physicians

2011· article· en· W2167662493 on OpenAlexafffund
Alice Gaudine, Sandra LeFort, Marianne Lamb, Linda Thorne

Bibliographic record

VenueNursing Ethics · 2011
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsYork UniversityQueen's UniversityMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchMemorial University of Newfoundland
KeywordsPerspective (graphical)NursingPsychologyHealth careEthical issuesMedicinePolitical scienceEngineering ethics

Abstract

fetched live from OpenAlex

Nurses and physicians may experience ethical conflict when there is a difference between their own values, their professional values or the values of their organization. The distribution of limited health care resources can be a major source of ethical conflict. Relatively few studies have examined nurses' and physicians' ethical conflict with organizations. This study examined the research question 'What are the organizational ethical conflicts that hospital nurses and physicians experience in their practice?' We interviewed 34 registered nurses, 10 nurse managers, and 31 physicians as part of a larger study, and asked them to describe their ethical conflicts with organizations. Through content analysis, we identified themes of nurses' and physicians' ethical conflict with organizations and compared the themes for nurses with those for physicians.

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)0.019
metaresearch head score (Gemma)0.038
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0190.024
Scholarly communication0.0110.008
Open science0.0020.008
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
GPT teacher head0.543
Teacher spread0.296 · 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
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

Citations48
Published2011
Admission routes2
Has abstractyes

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