Characterizing the development and dissemination of dietary messaging in the Inuvialuit Settlement Region, Northwest Territories
Bibliographic record
Abstract
Public health communication about diet in Inuit communities must balance the benefits and risks associated with both country and store-bought food choices and processes to support Inuit well-being. An understanding of how dietary messages—public health communication addressing the health and safety of country and store-bought food—are developed and disseminated in the Arctic is currently lacking. As part of the Country Foods for Good Health study, this participatory research sought to characterize dietary messaging in the Inuvialuit Settlement Region (ISR), Northwest Territories (NWT), from the perspective of territorial, regional and local dietary message disseminators to further improve message communication in the region. We conducted an in-person interview (n=1) (February 2020), telephone interviews (n=13) (May-June 2020), and follow-up telephone interviews (n=5) (June 2021) with key informants about their involvement in developing and/or disseminating dietary messages about the health benefits and risks of country foods and/or store-bought foods in/for the ISR. Key informants interviewed included health professionals (n=5), government employees (n=6) and community nutrition or food program coordinators (n=3) located in Inuvik, Tuktoyaktuk, Paulatuk and Yellowknife, NWT. We conducted a thematic analysis on the 19 interviews. Our findings indicate that publicly disseminated dietary messages in the ISR are developed at all scales and communicated through a variety of methods. Dietary messages focus predominantly on encouraging healthy store-bought food choices and conveying nutritional advice about store-bought and country foods. As federal and territorial messaging is seldom tailored to the ISR, representation of the Inuvialuit food system and consideration of local food realities is generally lacking. There is a need to evaluate dietary messages and improve collaborations among Inuvialuit country food knowledge holders, researchers, and public health dietary message disseminators at all scales to develop more locally tailored and culturally relevant messaging in the ISR. We recommend utilizing a participatory, collaborative, culture-centered approach to dietary message development and dissemination in northern Indigenous contexts.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".