Omorodion et al. Building HIV/AIDS Competent Communities by Youths African Journal of Reproductive Health June 2012 (Special Edition); 16(2): 71 ORIGINAL RESEARCH ARTICLE The Use of National Youth Service Corp Memb
Bibliographic record
Abstract
This paper focuses on the community component of a larger action research project on HIV Prevention for Rural Youth (HP4RY), funded by the Global Health Research Initiative (Canada). It began with ethnographic research in 10 communities selected using geographic representative sampling and random assignment to one of three research arms. Using the AIDS Competent Community (ACC) model developed by Catherine Campbell, the ethnographic research identified factors in six domains that contributed to youth vulnerability to HIV infection. This was followed by recruitment, training and deployment of three overlapping cohorts of young adults (n=40) serving in Nigeria’s National Youth Service Corp (NYSC), to mobilize youth and adults in the communities to increase communities’ AIDS competence over a nearly 2 year period. Monthly reports of these Corpers, observations of a Field Coordinator, and community feedback supported the conclusion that communities moved towards greater AIDS competence and reduction in youth vulnerability to HIV infection (Afr J Reprod Health 2012 (Special Edition);
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".