The Social-Ecological Model: Faith and the Targeted Prevention and Treatment of Cardiovascular Risk in Low- and Middle-Income Countries
Bibliographic record
Abstract
This paper examined how the use of the social-ecological model may facilitate best practices while integrating faith in targeted prevention and management of cardiovascular risk in low- and middle-income countries as most faith-based health institutions in developing countries promote the integration of faith in their population-level disease prevention programs. Given the rigid practice of faith in low- and middle-income countries, we recommend the adoption of the social-ecological model, which emphasizes the consideration of the various levels of influence (such as intrapersonal, interpersonal, organizational, community, and public policy) and the concept that people’s social environment influences their behaviors. Our recommendation is based on studies from diverse cultures that suggest that targeted prevention and management of risk factors, such as hypertension and hypercholesterolemia, might reduce morbidities and mortalities from cardiovascular diseases. This model is sustainable in low- and middle-income countries and aligns with the patients’ relationship with their creator and their neighbors while promoting best practices. Received: 20 April 2023 / Accepted: 23 June 2023 / Published: 5 July 2023
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".