Achieving cultural safety in Aboriginal health services: implementation of a crosscultural safety model in a hospital setting
Bibliographic record
Abstract
Genuine cross-cultural competency in health requires the effective integration of traditional and contemporary knowledge and practices. This paper presents an analytical framework that aims to enhance the ability of patients/clients, providers, administrators and policy makers to make appropriate choices, and to find pathways to true healing while ensuring that the required care is competently, safely and successfully provided. The examples presented are primarily based on the experience of the Sioux Lookout Meno Ya Win Health Centre (SLMHC), which serves a diverse, primarily Anishnabe population living in 32 northern Ontario communities spread over an area of 385 000 km2 of Canada. The SLMHC has a specific mandate, among Ontario hospitals, to provide a broad set of services that address the health and cultural needs of a largely First Nations population. We describe our journey to date to implement our comprehensive minoyawin model of care, including an evaluation of the initial outcomes. Minoyawin is an Anishnabe term that denotes health, wellness or well-being – a state of wholeness in the spiritual, mental, emotional and physical make-up of the person. The model focuses on cross-cultural integration in five key aspects of all of our services: Odabidamageg (governance and leadership) Wiichi’iwewin (patient and client supports) . Andaw’iwewin (traditional healing practices) Mashkiki (traditional medicines) Miichim (traditional foods). The paper outlines a continuum of programme development and implementation that has allowed core elements of our programming to be effectively integrated into the fabric of all that we do. Outcomes to date and practices that are potentially transferable are identified.
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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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".