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Record W2801335224 · doi:10.5206/uwomj.v86i2.2006

Moral distress in health care professionals

2017· article· en· W2801335224 on OpenAlexvenueaboutno aff
Ann Marie Corrado, Monica L. Molinaro

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsDistressCoping (psychology)Health careAction (physics)ContemplationPhenomenonPsychologyMoral responsibilityNursingSocial psychologyMedicinePublic relationsPolitical sciencePsychotherapistLawEpistemology

Abstract

fetched live from OpenAlex

Thousands of health care providers currently live and practice in Canada,1 and each day these providers are presented with new situations from their patients and clients. Many of these situations require much contemplation, and often both personal and professional judgment is used to come to a conclusion. In many cases, the decision-making process becomes difficult due to personal and professional beliefs, as well as institutional and legal requirements placed upon the health care provider. This phenomenon, known as moral distress, is “when one knows the right thing to do, but institutional constraints make it nearly impossible to pursue the right course of action”.2 This work provides a brief introduction to the topic of moral distress, the systemic factors that can lead to the development of moral distress, how it manifests in health care providers, and coping mechanisms used by health care providers to manage their moral distress.

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)0.010
metaresearch head score (Gemma)0.038
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 score0.017
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.023
Scholarly communication0.0050.003
Open science0.0010.009
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.461
Teacher spread0.375 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

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