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Record W2122920562 · doi:10.1186/cc2168

The role of teams in resolving moral distress in intensive care unit decision-making.

2003· article· en· W2122920562 on OpenAlexaff
Mary van Soeren, Adèle Miles

Bibliographic record

VenueCritical Care · 2003
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsSt. Thomas HospitalYork University
Fundersnot available
KeywordsBlameMedicineValue (mathematics)Moral dilemmaNursingEthical decisionPresentation (obstetrics)PsychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Conflicts arise within teams and with family members in end-of-life decision-making in critical care. This creates unnecessary discomfort for all involved, including the patient. Treatment plans driven by crisis open the team up to conflict, fragmented care and a lack of focus on the patient's wishes and realistic medical outcomes. Methods to resolve these issues involve planned ethical reviews and team meetings where open communication, clear plans and involvement in decision-making for all stakeholders occur. In spite of available literature supporting the value of these techniques, patient care teams and families continue to find themselves involved in spiraling conflict, pitting one team against another, placing blame on family members for not accepting decisions made by the team and creating moral conflict for interdisciplinary team members. Through a case presentation, we review processes available to help resolve conflict and to improve outcome.

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)0.034
metaresearch head score (Gemma)0.097
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 score0.034
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.010
Scholarly communication0.0070.007
Open science0.0010.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.504
Teacher spread0.436 · 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

Citations20
Published2003
Admission routes1
Has abstractyes

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