Applying cultural safety beyond Indigenous contexts: Insights from health research with Amish and Low German Mennonites
Bibliographic record
Abstract
People who identify as members of religious communities, such as the Amish and Low German Mennonites, face challenges obtaining quality health care and engagement in research due in part to stereotypes that are conveyed through media and popular discourses. There is also a growing concern that even when these groups are engaged in research, the guiding frameworks of the research fail to consider the sociocultural or historical relations of power, further skewing power imbalances inherent in the research relationship. This paper aims at discussing the uses of cultural safety in the context of health research and knowledge translation with groups of people that are associated with a specific religion. Research with the Amish and Low German Mennonites is provided as examples to illustrate the use of cultural safety in this context. From these examples, we discuss how the use of cultural safety, grounded in critical theoretical perspectives, offers new insight into health research with populations that are traditionally labeled as minority, vulnerable, or marginalized, especially when a dominant characteristic is a unique religious perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".