Storekeeper perspectives on improving dietary intake in 12 rural remote western Alaska communities: the “Got Neqpiaq?” project
Bibliographic record
Abstract
Low intake of fruits and vegetables and high intake of sugar-sweetened beverages persists as a public health concern in rural remote Alaska Native (AN) communities. Conducting key informant interviews with 22 storekeepers in 12 communities in the Yukon-Kuskokwim region of Alaska, we explored potential factors impeding or facilitating dietary change towards healthier food choices. We selected these sites as part of a multi-level intervention aimed at introducing more traditional AN subsistence foods, increasing fruit and vegetable intake, and decreasing SSB consumption among young children enrolled in Head Start (preschool) programmes (Clinicaltrials.gov #NCT03601299). Storekeepers in these communities agreed that seasonality and flight schedules were primary factors determining commercial foods' availability. Several storekeepers noted that federal food assistance programmes that specify which food items may be purchased with funds received from the programme and community policies that set limits on less healthy items promote customer purchases of healthier products. The fact that storekeepers are comfortable enforcing government assistance programme guidelines, company policies, and tribal resolutions suggests an important role storekeepers play in improving nutritional intake in their communities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".