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Record W2025563565 · doi:10.1207/s15327019eb1503_1

To Stay or To Go, To Speak or Stay Silent, To Act or Not To Act: Moral Distress as Experienced by Psychologists

2005· article· en· W2025563565 on OpenAlexaboutno aff
Wendy Austin, Marlene Rankel, Leon Kagan, Vangie Bergum, Gillian Lemermeyer

Bibliographic record

VenueEthics & Behavior · 2005
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsSilencePsychologyDistressContext (archaeology)Moral disengagementSocial psychologyMoral injuryMental healthPsychotherapist

Abstract

fetched live from OpenAlex

The moral distress of psychologists working in psychiatric and mental health care settings was explored in an interdisciplinary, hermeneutic phenomenological study situated at the University of Alberta, Canada. Moral distress is the state experienced when moral choices and actions are thwarted by constraints. Psychologists described specific incidents in which they felt their integrity had been compromised by such factors as institutional and interinstitutional demands, team conflicts, and interdisciplinary disputes. They described dealing with the resulting moral distress by such means as silence, taking a stance, acting secretively, sustaining themselves through work with clients, seeking support from colleagues, and exiting. Recognizing moral distress can lead to a significant shift in the way we perceive moral choices and understand the moral context of practice.

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)0.008
metaresearch head score (Gemma)0.021
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.023
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.315
GPT teacher head0.615
Teacher spread0.300 · 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

Citations129
Published2005
Admission routes1
Has abstractyes

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