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Record W2158328669 · doi:10.1177/1049732309349809

Dignity Violation in Health Care

2009· article· en· W2158328669 on OpenAlexaff
Nora Jacobson

Bibliographic record

VenueQualitative Health Research · 2009
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsDignityDismissalHealth careEmbeddednessRhetoricContext (archaeology)ContemptObjectificationPrejudice (legal term)SociologySocial psychologyPsychologyNursingLawPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

In this grounded theory analysis I sought to understand dignity violation in health care and to explore the context in which such violations take place. I found that dignity violation in health care occurs through processes of rudeness, indifference, condescension, dismissal, disregard, dependence, intrusion, objectification, restriction, labeling, contempt, discrimination, revulsion, deprivation, assault, and abjection. The conditions that promote these processes reside in the positions of the actors involved; in the asymmetrical relationships between the actors; in the health care setting itself, which is characterized by multiple tensions-including those between needs and resources, crisis and routine, experience and expertise, and rhetoric and reality; and in the embeddedness of health care in a broader social order of inequality. These findings suggest several interventions that might mitigate dignity violation in health care.

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)0.036
metaresearch head score (Gemma)0.033
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 score0.036
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.033
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.057
Scholarly communication0.0080.010
Open science0.0020.011
Research integrity0.0030.005
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 head0.631
GPT teacher head0.668
Teacher spread0.037 · 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

Citations151
Published2009
Admission routes1
Has abstractyes

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