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Record W3046968277 · doi:10.7565/ssp.2020.2815

Indigenous Cultural Safety Training in Health, Education, and Social Service Work

2020· article· en· W3046968277 on OpenAlexaff
Andrea Bowra, Lisa Howard, Angela Mashford‐Pringle, Erica Di Ruggiero

Bibliographic record

VenueSocial Science Protocols · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOperationalizationConceptualizationCINAHLIndigenousSocial workPublic relationsSociologyEngineering ethicsMedical educationMEDLINEMedicinePolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Background: Indigenous Cultural Safety (ICS) training is a growing field of study; however, little consensus exists about how ICS is conceptualized and operationalized. This lack of consistency can lead to misinterpretation and misappropriation of Indigenous knowledges and histories that can further perpetuate colonial harms.
 Objective: The objective of this scoping review is to explore and characterize the academic literature related to the conceptualization and operationalization of ICS training within the fields of health, social services, and education.
 Methods: This scoping review protocol employs the Joanna Briggs Institute’s three-step search strategy to identify articles in the following databases: MEDLINE, EMBASE, CINAHL, ERIC, and ASSIA. This protocol follows the PRISMA guidelines for Scoping Reviews (Joanna Briggs Institute, 2015; Tricco et al., 2018).
 Discussion: This review will add new knowledge by offering insights into the historic and contemporary approaches to defining and operationalizing ICS training in the health, education and social services fields. The results produced will be of interest to scholars and health, social services, and education providers looking to apply the most current and appropriate concepts and practices of ICS.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.097
GPT teacher head0.490
Teacher spread0.393 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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