Indigenous Cultural Safety Training for Applied Health, Social Work and Education Professionals: A PRISMA Scoping Review
Bibliographic record
Abstract
ABSTRACT Background Anti-Indigenous racism is a widespread social problem in health, social work, and education systems in English-speaking Colonized countries such as Canada, with profound negative impacts to the health and education of Indigenous peoples. In 2015, Canada’s Truth and Reconciliation Commission recognized the legacy and impact of Colonization and recommended training programs for these professions on cultural competency and curricula, and on the colonial history of Canada. Yet there is little evidence on best practices for such training, highlighting the need to synthesize existing findings on how these training programs are developed, implemented, and evaluated. Methods This scoping review explored the academic literature on Indigenous cultural safety and competence training in the health, social work, and education fields. Medline, EMBASE, CINAHL, ERIC and ASSIA were searched for articles published between 1996-2020 in Canada, United States, Australia, and New Zealand. The Joanna Briggs Institute’s three-step search strategy was used as was the PRISMA extension for Scoping Reviews. Data was charted and synthesized in three stages. Results 134 were included in this review. Data was extracted on four themes: 1) Article Characteristic; 2) Cultural Safety Concepts, Critiques and Rationale; 3) Characteristics of Cultural Safety Training; and 4) Evaluation Details of Cultural Safety Training. Findings suggest that research on cultural safety training programs in health, social work and education has grown significantly. Nursing and medicine professions have received a significant proportion of cultural training programs, compared with general/allied health, social work, and education. Across fields, professionals and students were targeted equally by training programs. Only half of evaluations of cultural safety and related intervention identified methodological limitations. Implications Considering, comparing, and contrasting literature on cultural safety and related concepts and how they are applied in practice would advance this scholarly work, as would more robust evaluations of cultural safety and similar training interventions to understand their impact at the individual level. Finally, commitment to meaningfully engage Indigenous communities to develop, implement and evaluate such programs is urgently needed.
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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 | 0.078 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".