Food consumption and nutritional status of sedentarized Baka Pygmies in Southern Cameroon: wild foods are less important for those who farm
Bibliographic record
Abstract
The sedentarization of Pygmies in the Congo Basin has triggered a profound transformation in their traditional lifestyles, particularly affecting dietary habits and food consumption. We employed 24-hour dietary recalls in 10 sedentarized Baka Pygmy villages in southeastern Cameroon, gathering data on diet composition, diversity (Household Dietary Diversity Score, HDDS), and nutrient intake per adult male equivalent (AME) from 67 homes (28% of all households). Our findings revealed that 62% of consumed foods were agricultural produce, 29% were locally produced or purchased products, and the remaining 9% comprised items sourced or hunted from the wild. The average HDDS per village was low (4.1±1.56) and mean total energy intake was 1734.9±1,031.8 kcal/AME, with significant contributions from cultivated foods. There was a negative correlation between the consumption of cultivated and wild foods. Moreover, a considerable proportion of households (78.7%, ranging from 22.4% to 97%) exhibited nutrient consumption below the lower 95% uncertainty interval found in a Cameroonian nutrient supply study. Additionally, 78.3% of respondents fell below WHO/FAO recommendations for 21 nutrients, even after adjusting for the Baka’s shorter stature. This high prevalence of insufficient nutrient intake underscores the urgent need for targeted interventions to address nutritional deficiencies within this population. We show Baka households rely more on cultivated foods and are less dependent on wild sources. Understanding the profound transformation in dietary patterns and its repercussions on the health and overall well-being of the studied marginalized Indigenous communities is pivotal in devising strategies to enhance their survival. This shift in dietary profiles often stems from complex factors, including socioeconomic challenges, environmental changes, and cultural shifts. To address these issues effectively, a comprehensive approach that integrates cultural sensitivity, community engagement, and sustainable practices is imperative.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".