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Record W2167358122 · doi:10.1177/0969733008097990

An Overview of Moral Distress and the Paediatric Intensive Care Team

2008· article· en· W2167358122 on OpenAlexafffund
Wendy Austin, Julija Kelečević, Erika Goble, Joy Mekechuk

Bibliographic record

VenueNursing Ethics · 2008
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsCapital District Health AuthorityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsTeamworkArgument (complex analysis)PsychologyNeonatal intensive care unitPower (physics)NursingEngineering ethicsMedicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

A summary of the existing literature related to moral distress (MD) and the paediatric intensive care unit (PICU) reveals a high-tech, high-pressure environment in which effective teamwork can be compromised by MD arising from different situations related to: consent for treatment, futile care, end-of-life decision making, formal decision-making structures, training and experience by discipline, individual values and attitudes, and power and authority issues. Attempts to resolve MD in PICUs have included the use of administrative tools such as shift worksheets, the implementation of continuing education, and encouragement to report. The literature does not yet show these approaches to be effective in the resolution of MD. The need to acknowledge MD among PICU teams is discussed and an argument made that, to facilitate understanding among team members, practice stories need to be shared.

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)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0030.005
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0040.004
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 head0.389
GPT teacher head0.570
Teacher spread0.182 · 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

Citations100
Published2008
Admission routes2
Has abstractyes

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