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Korean nurses' perceptions of ethical problems: Toward a new code of ethics for nursing

2000· article· en· W2054800016 on OpenAlexaboutno aff
Wonhee Lee, Marion Pope, Sung‐Suk Han, Soon‐Ok Yang

Bibliographic record

VenueNursing and Health Sciences · 2000
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsEthical codeNursingEthical issuesPerceptionPsychologyCode (set theory)Nursing practiceNursing ethicsNurse educationMedicineEngineering ethicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract The Nursing Philosophy, Theory, History and Ethics Interest Group of the Alpha Lambda Chapter of Sigma Theta Tau in Seoul undertook a study of Korean nurses' perceptions of ethical problems in nursing practice, administration, education and research in 1993. The purposes of the study were to develop the ethical concerns of Korean nurses and to make suggestions for contents and guidelines for a new code. The data consisted of 329 descriptive items of ethical problems summarized into 221 descriptions and 57 nurses responded. To meet the study objectives, we analyzed the data according to content themes, comparing these data with the code of the Korean Nurses' Association (KNA), to the codes of the Canadian Nurses' Association and the College of Nurses of Ontario. In conclusion, the result showed that a new KNA code needs to include guidelines at different levels of abstractness, including standards at a concrete level reflecting the ethical problems faced by nurses in practice, administration, education, and research.

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)0.069
metaresearch head score (Gemma)0.113
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 score0.069
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.113
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0050.013
Scholarly communication0.0080.007
Open science0.0010.009
Research integrity0.0020.006
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 head0.395
GPT teacher head0.617
Teacher spread0.222 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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