Paddling Together for Culturally Safe Emergency Care for Elders
Bibliographic record
Abstract
This paper describes a wise practice for collaborative change through the Trauma- Informed and Culturally Safe Emergency Care for Nuu-chah-nulth Elders project. For decades, Nuu-chah-nulth Elders have been avoiding emergency care due to colonial trauma and a lack of culturally safe care. To begin addressing this community priority, the First Nations Health Authority, in partnership with Island Health and university partners, organized a two-day workshop in September 2017 with Nuu-chah-nulth Elders, community members, and health partners. Key to ensuring the process was culturally sensitive was following the guidance of the West Coast General Hospital Cultural Safety Committee, a partnership between Nuu-chah-nulth people and health providers. Respect and trust were developed by centring the voices of Elders and giving them a safe space for discussion before developing recommendations with health partners. Feedback from participants was gathered from notes and audio recordings and thematically analyzed into eight major recommendations (i.e., increase engagement and relationship building; develop action plans; increase education and awareness; increase advocacy and support; incorporate First Nations medicine, healing, and foods; provide culturally safe spaces; develop policy and protocol; and link to comprehensive community support), with attention to preserving Elder voices. The recommendations were validated by returning and new participants at a gathering in June 2019. Elders noted that while experiences of unsafe care continue, noticeable improvements in cultural safety are being made and they feel heard.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".