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Record W2507923665 · doi:10.1111/nin.12144

Disciplining virtue: investigating the discourses of opioid addiction in nursing

2016· article· en· W2507923665 on OpenAlexaff
Diane Kunyk, Margaret Milner, Alissa Overend

Bibliographic record

VenueNursing Inquiry · 2016
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsAddictionFraming (construction)PsychologyPower (physics)Health careSociologyCriminologyNursingPublic relationsMedicinePolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

Two nurses diagnosed with opioid addiction launched legal action after being found guilty of unprofessional conduct due to addiction-related behaviors. When covered by the media, their cases sparked both public and legal controversies. We are curious about the broader discursive framings that led to these strong reactions, and analyze the underlying structures of knowledge and power that shape the issue of opioid addiction in the profession of nursing through a critical discourse analysis of popular media, legal blogs and hearing tribunals. We argue that addiction in nursing is framed as personal choice, as a failure in the moral character of the nurses, as decontextualized from addiction as disease arguments, and as an individualized issue devoid of contextual factors leading to addiction. Our investigation offers a critical case study of a nursing regulatory body that upheld popular assumptions of addiction as an autonomous, rational choice replete with individual-based consequences - a framing that is inconsistent with evidence-based practice in health-care. We put forth this critical interrogation to open up possibilities for counterdiscourses that may promote more nuanced and effective responses to the issue of addiction in nursing.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.356
Teacher spread0.322 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2016
Admission routes1
Has abstractyes

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