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Record W4288063526 · doi:10.1097/cnq.0000000000000389

The Attitude of Iranian Critical Care Nurses Toward Euthanasia

2021· article· en· W4288063526 on OpenAlexaff
Amir Emami Zeydi, Mohammad Javad Ghazanfari, Olive Fast, Saman Maroufizadeh, Keyvan Heydari, Mohammad Hashem Gholampour, Samad Karkhah

Bibliographic record

VenueCritical Care Nursing Quarterly · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMedicineIntensive care unitPityScale (ratio)NursingIntensive careCritical care nursingOpposition (politics)Health careFamily medicinePsychologyPsychiatrySocial psychologyIntensive care medicine

Abstract

fetched live from OpenAlex

Today, one of the major ethical challenges facing the world's health care system, and in particular nurses in the intensive care unit, is euthanasia or death out of pity. The aim of this study was to investigate the attitude of Iranian nurses in the intensive care unit toward euthanasia. This was an analytical cross-sectional study using census sampling. The data collection tool was the Euthanasia Attitude Scale. A total of 206 nurses working in the intensive care unit in 4 hospitals in the Mazandaran province of Iran were included in this study. The mean of total Euthanasia Attitude Scale score in intensive care unit nurses was 2.96. The mean euthanasia dimensions were ethical consideration, practical considerations, treasuring life, and naturalistic beliefs, 3.03, 2.92, 2.98, and 2.99, respectively. There was significant but low negative correlation between age and total Euthanasia Attitude Scale score, ethical considerations, and practical considerations. Male nurses exhibited significantly higher Euthanasia Attitude Scale scores, specifically in regard to ethical and practical considerations compared with female nurses. The most Iranian nurses in the intensive care unit had a negative attitude toward euthanasia for patients in the later stages of the disease. However, this opposition was less than similar studies in Iran in the past.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.154
GPT teacher head0.486
Teacher spread0.332 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

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