Ceremony, Respect, and Understanding: Storytelling as Method for Indigenous Patient Experience Measurement to Inform Culturally Safe Care
Bibliographic record
Abstract
Globally, it is essential that Indigenous-specific patient healthcare experience metrics are developed, to ensure that the distinct experiences of Indigenous Peoples in healthcare are captured appropriately. In this study, we sought to understand Indigenous Elders' perspectives on storytelling as a way to measure and respond to Indigenous patients’ healthcare experiences. This study is led by a team of Indigenous and non-Indigenous researchers, committed to using culturally safe approaches throughout all stages of research. Four individual interviews were conducted with Indigenous Elders who serve patients in the healthcare system in Vancouver, British Columbia (BC), Canada, and thematic and narrative analyses were used to identify themes pertaining to the use of storytelling in the Elders’ work, and as an approach to measuring Indigenous patients’ experiences. Four BC First Nations Elders participated in one-on-one in-depth interviews between March and August 2023. Elders shared that an important element of storytelling is the ability to centre positive conversation to help with a patients’ healing journey. Storytelling, which represents a traditional practice of knowledge translation for many First Nations, Métis, and Inuit communities, was found to connect Indigenous Peoples to cultural teachings and supports, which allowed for the building and strengthening of relationships between patients, families, and healthcare providers. Findings illustrate that storytelling is an important and effective strategy for measuring and responding to Indigenous patients’ healthcare experiences. Study results support the use of storytelling as a method for measuring Indigenous patients’ healthcare experiences, and future research should focus on understanding community perspectives and logistics of implementing a storytelling-based, Indigenous-specific patient experience measurement tool.
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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 | 0.041 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".