Indigenous Cultural Safety Trainings for Healthcare Professionals Working in Ontario, Canada: Context and Considerations for Healthcare Institutions
Bibliographic record
Abstract
Background: Racism and discrimination are realities faced by Indigenous peoples navigating the healthcare system in Canada. Countless experiences of injustice, prejudice, and maltreatment calls for systemic action to redress professional practices of health care professionals and staff alike. Research points to Indigenous cultural safety training in healthcare systems to educate, train, and provide non-Indigenous trainees the necessary skills and knowledge to work with and alongside Indigenous peoples using cultural safe practices grounded in respect and empathy. Objective: We aim to inform the development and delivery of Indigenous cultural safety training within and across healthcare settings in the Canadian context, through repository of Indigenous cultural safety training examples, toolkits, and evaluations. Methods: An environmental scan of both gray (government and organization-issued) and academic literature is employed, following protocols developed by Shahid and Turin (2018). Synthesis: Indigenous cultural safety training and toolkits are collected and described according to similar and distinct characteristics and highlighting promising Indigenous cultural safety training practices for adoption by healthcare institutions and personnel. Gaps of the analysis are described, providing direction for future research. Final recommendations based on overall findings including key areas for consideration in Indigenous cultural safety training development and delivery. Conclusion: The findings uncover the potential of Indigenous cultural safety training to improve healthcare experiences of all Indigenous Peoples. With the information, healthcare institutions, professionals, researchers, and volunteers will be well equipped to support and promote their Indigenous cultural safety training development and delivery.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".