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Record W7065251332

Development of an educational resource: cultural safety with patients who identify as Black, African Nova Scotian, African or Caribbean descent

2023· report· en· W7065251332 on OpenAlexafffundabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typereport
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsContext (archaeology)Health carePopulationPublic healthEthnic groupAgency (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Background: Black patients have faced health disparities and mistreatment in healthcare settings (Public Health Agency of Canada, 2020). The United Nations (UN) declared a decade for people of African descent from 2015-2024 (UN, n.d.). The UN called for recognizing this distinct population and protecting and promoting their human rights (UN, n.d.). Racial discrimination and microaggressions contribute to current and historical mistrust in the healthcare system (CDC, 2020; Cénat et al., 2022a; Cénat et al., 2022b; Waldron et al., 2023; Wolinetz & Collins, 2020). To improve the health experiences of Black patients, healthcare staff must create culturally safe environments. Methods: I conducted a literature review, consultations, and an environmental scan to explore and analyze the healthcare experiences of Black patients and the experiences of healthcare staff working with Black patients. Through the literature review, I focused on global experiences while I explored the local context through the environmental scan and consultations. Results: Black patients experienced racial discrimination, microaggressions, and a lack of trust in the health system (CDC, 2020; Cénat et al., 2022a; Cénat et al., 2022b; Waldron et al., 2023; Wolinetz & Collins, 2020). The mental health impacts of these experiences included anxiety, depressive symptoms, sleep problems, and decreased help-seeking (Cénat et al., 2022a; Cénat et al., 2022b; Chan et al., 2023; Moody et al., 2022; Nguyen et al., 2023; Waldron et al., 2023; Washington & Randall, 2023). Cultural safety training can increase healthcare providers' knowledge and improve patient interactions (Browne et al., 2021; Kaihlenan et al., 2019; Pimental et al., 2022; Yaphe et al., 2019). Based on these results, I created an educational resource, including a PowerPoint presentation and teaching guide for a one-day course in cultural safety with Black patients.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0030.003
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0350.006

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 head0.066
GPT teacher head0.315
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes3
Has abstractyes

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