Appalachian Foodways From Then To Now: Using Traditional Foods To Enhance Dietetic Practice
Bibliographic record
Abstract
Introduction: Convenience and fast foods have slowly worked their way into the rural Appalachian diet playing a major role in increased obesity and food-related ailments. Increasing future health providers’ knowledge of historical Appalachian dietary patterns and how health is perceived in their rural patients may lead to the development of culturally-sensitive diet therapy when working with rural populations. Methods: To verify the Traditional Southern Appalachian Diet Pyramid and to determine the composition of these dietary patterns, historical cookbooks, articles, and oral history interviews were qualitatively analyzed. Food items were coded into categories on the Diet Pyramid using qualitative analysis software. Results: Final analysis divulges the four largest categories of the traditional Appalachian diet: home grown produce, added fats and sugars, sources of protein, and sources of carbohydrates. Within these categories corn, potatoes, green beans, fat back, soup beans, and pork are some of the ingredients most commonly consumed. Discussion: Findings reveal the traditional Appalachian diet was plant-based, home-grown or gathered produce with the addition of added fats and sugars, protein, and carbohydrate sources fleshing out the diet. Future research endeavors can utilize these findings for developing culturally-sensitive nutrition interventions in rural patients seeking diet therapy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".