Indigenous Cultural Safety Training in Health, Education, and Social Service Work
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".