Five ways to get a grip on the need to include clinical placements in Indigenous settings
Bibliographic record
Abstract
Educational organizations that train medical professionals are intricately linked to the responsibility of creating culturally safe healthcare providers. However, prevailing inequities contribute to the continued oppression of Indigenous peoples, evidenced by inequitable access, treatment, and outcomes in the healthcare system. Despite an increasing awareness of how colonialist systems and the structures within them can contribute to health disparities, this awareness has not led to drastic improvements of health outcomes for Indigenous peoples. Many recently graduated health professionals will have likely encountered Indigenous peoples as a minority population within the larger, non-Indigenous context. Clinical placements in Indigenous settings may improve recruitment and retention of healthcare professionals in rural and remote settings, while helping educational institutions fulfill their social accountability missions. These placements may aid in the decolonization of care through reductions in bias and racism of medical professionals. Clinical placements in Indigenous settings may better prepare providers to navigate the dynamic challenges of the healthcare needs of Indigenous peoples safely and respectfully.
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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 | 0.089 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.026 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.009 | 0.037 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.021 | 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".