Community-based Interventions – International Development, Government and Community
Bibliographic record
Abstract
Non-governmental organizations (NGOs) are often criticized for their work in the healthcare sectors of developing countries. Recently, they have been moving from a projectized to sectoral support approach in an attempt to respond to this critique. Community-based intervention is a sectoral technique that can be used to exploit the community as a resource to provide the community itself with support in ways that the limited human resources in the healthcare sectors of developing countries currently cannot. The peer group intervention and community health worker are both tested strategies that have resulted in positive outcomes in the healthcare sectors of disadvantaged areas. Although community support systems may be limited in technical skill, a new body of research indicates that this intervention can nonetheless be a predictor of improved management of chronic illnesses that present high disease burden. By working with local stakeholders, NGOs may be able to effectively facilitate this intervention to promote sustainable positive health outcomes. By evaluating the effectiveness of the community-based intervention in Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) management in Malawi, a greater understanding of its role can be gained in a more tangible manner.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".