Identifying barriers to traditional game consumption in First Nation adolescents in remote northern communities in Ontario, Canada
Bibliographic record
Abstract
Objectives: To investigate factors influencing consumption of traditional foods (e.g., wild game, fish) and concerns about environmental contaminants among schoolchildren of the Mushkegowuk Territory First Nations (Moose Factory, Fort Albany, Kashechewan, Attawapiskat, and Peawanuck). \nStudy Design: Cross-sectional data collection from a Web-based Eating Behaviour Questionnaire (WEB-Q). \nMethods: Schoolchildren in grades 6-12 (n = 262) responded to four of the WEB-Q questions: (1) Do you eat game? (2) How often do you eat game? (3) How concerned are you about the environmental contaminants in the wild game and fish that you eat? (4) I would eat more game if...[ six response options]. Data were collected from 2004-2009. Hierarchical log-linear modelling (LLM) was used for analyses of multi-way frequency data. \nResults: Of the school children answering the specific questions: 174 consumed game; 95 reported concerns about contaminants in game; and 84 would increase their game consumption if it were more available in their homes. LLM revealed significant differences between communities; schoolchildren in Moose Factory consumed game “rarely or never” at greater than expected frequency, and fewer than expected consumed game “at least once a day.” Schoolchildren in Kashechewan had greater frequency of daily game consumption and few were concerned about contaminants in game. Using LLM, we found that sex was an insignificant variable and did not affect game consumption frequency or environmental contaminant concern. \nConclusion: The decreasing importance of the traditional diet was most evident in Moose Factory, possibly due to its more southerly location relative to the other First Nations examined.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".