Adaptations to the Serious Illness Conversation Guide to Be More Culturally Safe
Bibliographic record
Abstract
The Serious Illness Conversation Guide (SICG) has been shown to be an effective communication tool used by health care professionals when interacting with patients facing a life-limiting illness. However, Ariadne Labs, the originators of the tool, have not tested it with First Nations and Indigenous Peoples. In this project, the British Columbia Centre for Palliative Care and the First Nations Health Authority in British Columbia (BC), Canada collaborated to adapt the SICG to be more culturally safe for First Nations and Indigenous Peoples. Multiple feedback strategies were employed. Feedback was received from 35 older adults, Elders, and community members from two First Nations communities plus approximately 80 nurses serving in First Nations communities across BC. Key areas of focus for feedback on the clinical tool included setting up the conversation, involving family, closing the conversation, and using principles of health literacy to reduce power differences. Three questions were added in response to feedback received. By creating a safe space for dialogue, it is hoped that health care providers and family members will develop a deeper understanding of what is important to the person with a life-limiting illness. These conversations promote patient-centred health care that aligns with patient values and wishes. Findings from this project directly informed modification of the tool to support a more culturally safe conversation. Further research will inform whether this tool is culturally safe for all seriously ill people.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".