Behavioral Risk Factors and Non-communicable diseases in Tribal Adults: A Systematic Review
Bibliographic record
Abstract
Background: Non-communicable diseases (NCDs) are a significant public health concern globally, especially among tribal populations, where unique socio-cultural practices may influence the prevalence of behavioral risk factors. Understanding the relationship between these risk factors and NCDs is critical for targeted interventions. Objectives: This systematic review aims to synthesize the existing evidence on the prevalence of behavioural risk factors—such as tobacco use, alcohol consumption, unhealthy diets, and physical inactivity—among tribal adults and their association with NCDs, including cardiovascular diseases, diabetes, and cancer. Methods: A comprehensive search was conducted across databases such as PubMed, Scopus, and Google Scholar for studies published between 2000 and 2023. Inclusion criteria included peer-reviewed studies reporting on tribal adults (aged ≥18 years) and exploring the relationship between behavioural risk factors and NCDs. Exclusion criteria included studies with non-tribal populations and those focused on infectious diseases. Data extraction followed PRISMA guidelines, and the quality of included studies was assessed using the Newcastle-Ottawa Scale. Results: A total of X studies met the inclusion criteria, highlighting the significant burden of behavioural risk factors among tribal populations. Tobacco use and alcohol consumption were the most prevalent risk factors, with an associated increase in the incidence of cardiovascular diseases and diabetes. Limited access to healthcare and socio-economic challenges were identified as key barriers to addressing these risk factors. Conclusions: The review underscores the urgent need for culturally tailored public health interventions targeting behavioural risk factors in tribal populations to mitigate the rising burden of NCDs. Further research is needed to explore effective strategies for health promotion and disease prevention in these vulnerable communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".