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Record W2947369094 · doi:10.22374/jmhan.v3i1.33

Sowing a Seed of Safety: Providing Culturally Safe Care in Acute Care Settings for People who use Drugs

2019· article· en· W2947369094 on OpenAlexaffvenue
Jane McCall, Bernie Pauly

Bibliographic record

VenueJournal of Mental Health and Addiction Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSowingMedicineAcute careMedical emergencyHealth carePolitical scienceAgronomy

Abstract

fetched live from OpenAlex

This paper reviews the concept of cultural safety from the perspective of people who use illicit drugs and nurses in a hospital setting. Background Illicit drug use is often highly stigmatized and people who use illicit drugs often report negative healthcare experiences contributing to inequities in health and access to healthcare. Registered nurses play a key role in the delivery of healthcare when people who use drugs are hospitalized but often face difficulties in the provision of care. We explored understandings and meanings of cultural safety in healthcare as an approach to mitigate stigma and to promote health equity. Design and Methods Within an overall participatory approach to the research, we employed a qualitative ethnographic approach undertaking 275 hours of participant observation and conducting 34 open-ended interviews with 15 patients and 19 nurses on two acute care hospital units in 2012 and 2013. Result/Findings: Culturally safe care requires recognizing stereotypes and power imbalances; prioritizing trust and building relationships as important outcomes; giving patients space and time; and addressing conflicting organizational values and policies. Conclusions Providing culturally safe care requires organizational culture shifts that recognize the importance of historical, societal, and political forces that influence the way in which illicit drug use and people who use illicit drugs are constructed in society.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.339
Teacher spread0.325 · 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 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

Citations24
Published2019
Admission routes2
Has abstractyes

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