Bibliographic record
Abstract
Background: Strengths-based and culturally sensitive approaches to Indigenous healthcare are much needed within the Canadian healthcare system. This is where allyship comes in. Allyship is loosely defined as the actions of an individual who strives to advance the interests of marginalized groups in which they are not a member. This study investigated the concept of allyship with healthcare providers who were community-identified allies providing care for Indigenous patients. Methods: Qualitative description methodology was utilized, and data was generated through semi-structured interviews with allies in and around the Edmonton area, in Canada. The interviews were conducted online, transcribed verbatim and then coded using thematic analysis. Results: Interviews were conducted with 13 allies (eight physicians, four allied health professionals and one nurse). The results were captured into three main themes. The meaning of allyship demonstrated how allyship must be determined by the community, and encapsulates authentic action and advocacy, as well as working to create positive healthcare experiences. The experience of being an ally included commitment to the allyship journey, embracing emotions, and facing and disrupting systemic barriers. Finally, cultivating allyship in healthcare necessitated building and maintaining meaningful relationships with Indigenous people, and ongoing training and education. Conclusion: The study results enabled a better understanding of how allies interact with their Indigenous patients within the confines of the healthcare system and could inform learning opportunities for those who seek to practice in a culturally humble way. In particular, transcending passive education and training modalities to include opportunities for real life interactions and the development of reciprocal relationships with Indigenous patients.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".