3.8-W1Impacting clinical and cultural competencies through cross-cultural connections: the development of an International Indigenous Academic Health Network
Bibliographic record
Abstract
Background: The health disparities between Indigenous Canadians and the general Canadian population are strikingly similar to those experienced by Indigenous people around the world (1). It is this shared experience that underlies the development of this international network and the collective need to address health inequities and disparities. The University of Manitoba’s Max Rady College of Medicine in the Rady Faculty of Health Sciences is leading a project that seeks to further develop an International Academic Network in Indigenous Health Universities and health care institutions are largely based on colonial European culture and, as such, tend to place patients who are culturally or ethnically different from the mainstream at greater risk of experiencing adverse health events (2). The Network was established to stimulate knowledge creation and mobilisation in the area of Indigenous Health across the domains of medical education, health research and health service delivery. The Network also facilitates academic exchanges that will influence positive changes in approaches to Indigenous Health teaching, research and health service delivery through our Network Faculty Partnerships. The workshop discussion will focus on Indigenous health within the constructs of academic institutions that prepare health care professionals for practice in a diverse community. The workshop format will work well as the presenters will share some background on Indigenous Health, the limitations of current approaches and the benefits of an international academic network in supporting best practice in Indigenous Health. Objectives: To explore and discuss the role of national legislation, policies and services and the implications for action on the health and wellbeing of Indigenous Peoples in Canada. This will be presented within the context of how racial or ethnic health differences may result in inequalities and disparities, including discrimination, social exclusion and marginalisation of racial groups. To explore and to share the approaches to academic and health system responsiveness to diversity, the development of good clinical practice, and the development of professional training and education. Workshop Plan: The two presenters will share current information on the Canadian context and the development of the academic health network. Following the presentations, we will open a dialogue on approaches for cultural safety in the learning and work environment and for advocacy of equity in academic and health systems (3). The goal is to identify best practices that will mitigate the impact of bias in addressing racial or ethnic inequalities and health disparities. We will share the benefits of international collaboration on best practices in Indigenous Health. Main messages: The workshop dialogue will highlight the need for health care professionals and academics to advocate for equity and to advocate for a culturally safe approach to education, training and health service delivery. References 1. Reading, J. A global model and national network for Aboriginal health research excellence. Can J Public Health. 2003;94:185-9. 2. Svendson AC, Laberge M. Convening stakeholder networks: a new way of thinking, being and engaging. J Corp Citizen. 2005;19:91-104. 3. Walker R, Cromarty H, Kelly L, St Pierre-Hansen N. Achieving Cultural Safety in Aboriginal Health Services: implementation of a cross-cultural safety model in a hospital setting. Divers Health Care. 2009;6:11-22.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".