Healthy living in Nunavut: an on-line nutrition course for inuit communities in the Canadian arctic
Bibliographic record
Abstract
OBJECTIVES: It is recognized that empowerment of Indigenous Peoples through training and education is a priority. The objective was to design a course that would provide an innovative training approach to targeted workers in remote communities and enhance learning related to the Nunavut Food Guide, traditional food and nutrition, and diabetes prevention. STUDY DESIGN: A steering committee was established at the outset of the project with representation from McGill University and the Government of Nunavut (including nutritionists, community nurses and community health representatives (CHRs), as well as with members of the target audience. Course content and implementation, as well as recruitment of the target audience, were carried out with guidance from the steering committee. METHODS: An 8-week long course was developed for delivery in January - March, 2004. Learning activities included presentation of the course content through stories, online self-assessment quizzes, time-independent online discussions and telephone-based discussions. Invitations were extended to all prenatal nutrition program workers, CHRs, CHR students, home-care workers, Aboriginal Diabetes Initiative workers and public health nurses in Nunavut. RESULTS: Ninety-six health-care workers registered for Healthy Living in Nunavut, with 44 actively participating, 23 with less active participation and 29 who did not participate. CONCLUSIONS: Despite having to overcome numerous technological, linguistic and cultural barriers, approximately 40% of registrants actively participated in the online nutrition course. The internet may be a useful medium for delivery of information to target audiences in the North.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".