MétaCan
Menu
Back to cohort

Moral distress in the ICU: it's time to do something about it!

2020· article· en· W3003424276 on OpenAlexaff
Franco A. Carnevale

Bibliographic record

VenueMinerva Anestesiologica · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineDistressIntensive care medicineMedical emergencyClinical psychology

Abstract

fetched live from OpenAlex

Moral distress is a major concern among healthcare professionals (HCPs). In the Intensive Care Unit (ICU), moral distress can result from: 1) disagreements within the ICU team regarding life-sustaining treatments; 2) critical illnesses that result in tragic choices regarding treatment planning; 3) circumstances that require rapid decisions and actions without adequate consideration of all morally meaningful concerns; 4) tensions with administrators; and 5) legal standards that define the decisional authority that should be held by patients and families or which forms of end-of-life care are permissible. An impressive body of research literature has highlighted the prevalence of moral distress among HCPs (including ICU HCPs), health impacts of moral distress, as well as personal and contextual factors that are strong predictors of moral distress. However, there is a paucity of knowledge on effective ways to address moral distress. Yet, action is needed because many ICU HCPs are experiencing significant moral distress. This article outlines strategies that could be used to help diminish moral distress, drawing on the available literature. These strategies include: 1) Listen attentively to your colleagues' moral distress; 2) shift the focus from moral distress to moral agency; 3) promote ethically-attuned discussion and education (drawing on discussion models that can help reconcile diverse ethical viewpoints or disagreements); and 4) provide personal supports for HCPs. Research is urgently needed to further examine which strategies are most effective for addressing moral distress in ICU settings as well as other clinical contexts.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.476
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.008

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 head0.173
GPT teacher head0.458
Teacher spread0.285 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary · Editorial

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

Citations33
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMinerva AnestesiologicaSame topicEthics in medical practiceFrench-language works237,207