Improving hospital nutrition care through “Indigenous cultural safety” of menu options: Results of a cross‐sectional survey of Indigenous people in Western Canada
Bibliographic record
Abstract
BACKGROUND: Hospital nutrition services rarely offer Indigenous-specific menu options, an essential element of delivering "Indigenous cultural safety" in nutrition care to Indigenous patients. METHODS: Indigenous participants (n = 370) completed a semistructured Indigenous Food Ways survey (paper-based and online) from 2021 to 2022 as part of continuous healthcare quality improvement. Descriptive statistics summarized cultural/traditional foods respondents wanted to see or did not want to see as options on hospital menus. RESULTS: A majority of respondents (83%) agreed that a menu option for cultural/traditional foods was important, with 716 unique items reported. Seafood and meat/alternatives were common cultural/traditional items for menu options, specifically salmon (22%), traditional meat (14%), moose (12%), and venison (12%). Grain items reported were mostly Bannock (22%). Nearly 20% of the sample listed traditional foods, medicine, or tea as options for hospital menus. Respondents identified 254 items they did not want to see on hospital menus, which were commonly energy-dense/processed foods (16%) or poorly prepared/flavored items (13%), as well as peas and carrots (7%). Notably, some foods (eg, Bannock, salmon, fish eggs) were listed among both desired and not desired menu options. CONCLUSION: Results showed that offering cultural or traditional foods on hospital menus is considered important by Indigenous people, and that a wide range of foods would be desired. Specific cultural foods to include on menus were salmon, traditional meats, or traditional teas. Foods not to include were also identified. Improving future nutrition care and services will require menu modifications to provide culturally safe options for Indigenous patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".