Fulfilling cultural safety expectations in specialist medical education and training: considerations for colleges to advance recognition and quality
Bibliographic record
Abstract
Fulfilling cultural safety expectations in specialist medical education and training: considerations for colleges to advance recognition and quality Positionality statement A s a proud Biripi man, academic, doctoral student, and former medical practitioner working daily at the cultural interface, I perceive myself as a conscious mediator of conflicting Indigenous and Western health paradigms.The term "Indigenous" in this article respectfully and collectively refers to the First Peoples of lands which have been colonised, including Aboriginal and Torres Strait Islander, Māori and other First Nations peoples.My experiences within these spaces have formed a unique perspective that is continuously confirmed and challenged.As I educate in the Indigenous Health space, I am constantly reminded of the fact that I do not hold all knowledge, I am but a learner akin to the students I profess to teach.My experiences have moulded my understanding of what appropriate and effective health care for Indigenous peoples looks like to "me", but am critically aware that perspectives are myriad, dynamic and equally valid.
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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 | 0.065 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".