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Record W4323309453 · doi:10.1097/cce.0000000000000879

Moral Orientation, Moral Decision-Making, and Moral Distress Among Critical Care Physicians: A Qualitative Study

2023· article· en· W4323309453 on OpenAlexaffabout
Dominique Piquette, Karen E. A. Burns, Franco A. Carnevale, Aimee Sarti, Mika Hamilton, Peter Dodek

Bibliographic record

VenueCritical Care Explorations · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of British ColumbiaToronto Western HospitalOttawa HospitalCentre for Advancing Health OutcomesSt. Michael's HospitalMcGill UniversityHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsPsychologyCognitive dissonanceMoral disengagementSocial psychologyPsychological interventionThematic analysisMoral agencyDistressMoral courageSocial cognitive theory of moralityMoral reasoningQualitative researchClinical psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Moral distress is common among critical care physicians and can impact negatively healthcare individuals and institutions. Better understanding inter-individual variability in moral distress is needed to inform future wellness interventions. OBJECTIVES: To explore when and how critical care physicians experience moral distress in the workplace and its consequences, how physicians' professional interactions with colleagues affected their perceived level of moral distress, and in which circumstances professional rewards were experienced and mitigated moral distress. DESIGN: Interview-based qualitative study using inductive thematic analysis. SETTING AND PARTICIPANTS: Twenty critical care physicians practicing in Canadian ICUs who expressed interest in participating in a semi-structured interview after completion of a national, cross-sectional survey of moral distress in ICU physicians. RESULTS: Study participants described different ways to perceive and resolve morally challenging clinical situations, which were grouped into four clinical moral orientations: virtuous, resigned, deferring, and empathic. Moral orientations resulted from unique combinations of strength of personal moral beliefs and perceived power over moral clinical decision-making, which led to different rationales for moral decision-making. Study findings illustrate how sociocultural, legal, and clinical contexts influenced individual physicians' moral orientation and how moral orientation altered perceived moral distress and moral satisfaction. The degree of dissonance between individual moral orientations within care team determined, in part, the quantity of "negative judgments" and/or "social support" that physicians obtained from their colleagues. The levels of moral distress, moral satisfaction, social judgment, and social support ultimately affected the type and severity of the negative consequences experienced by ICU physicians. CONCLUSIONS AND RELEVANCE: An expanded understanding of moral orientations provides an additional tool to address the problem of moral distress in the critical care setting. Diversity in moral orientations may explain, in part, the variability in moral distress levels among clinicians and likely contributes to interpersonal conflicts in the ICU setting. Additional investigations on different moral orientations in various clinical environments are much needed to inform the design of effective systemic and institutional interventions that address healthcare professionals' moral distress and mitigate its negative consequences.

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)0.011
metaresearch head score (Gemma)0.017
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 score0.020
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.211
GPT teacher head0.589
Teacher spread0.378 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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