“Are They Just Experimenting With All of Us?” Cultural Considerations for Clinicians Caring for Seriously Ill Great Plains American Indians
Bibliographic record
Abstract
Context: Serious illnesses like cancer disproportionately affect American Indians and Alaska Native (AI/AN) Peoples. AI/AN patients deserve culturally responsive healthcare at all times, and especially when journeying through serious illness. Objectives: To learn about specific clinician-related factors that AI/AN cancer survivors, caregivers, Tribal leaders, and traditional healers want from their clinicians while experiencing cancer. Methods: We utilized qualitative interviews and Indigenous talking circles to explore perspectives on what type of clinician education, communication approaches, and clinical resources are desired so that clinicians may provide culturally responsive care to AI/AN peoples experiencing cancer. Analysis was completed via a team of Native and non-Native researchers analyzing narrative data from AI/AN cancer survivors, caregivers, Tribal leaders, and traditional healers. Results: Interviews and talking circle qualitative analysis revealed 3 major themes related to clinician needs: cultural considerations, psychosocial support, and trust. Conclusion: Any clinician caring for AI/AN peoples with serious illness such as cancer needs to understand clinician-related factors that AI/ANs say impact their care when experiencing serious illness. It is important for clinicians to engage in cultural education and work to improve systemic deficiencies such as a lack of psychosocial support. An overarching theme was also the need for clinicians to seek to develop trustworthiness and earn trust when caring for AI/AN patients experiencing serious illness.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".