[AIDS/HIV prevention and control among long-distance drivers in China: a Meta analysis].
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy of HIV/AIDS prevention and control among long-distance drivers in China. METHODS: Based on the principles and methods of Cochrane systematic reviews, we searched literature from CBM (from 1978 to 2009), VIP (1989 to 2009) CNKI (1980 to 2009), and MEDLINE (1950 to 2009) and also assessed the risk of bias of the included before-after studies according to Newcastle-Ottawa scale and their characteristics. Cochrane Collaboration's software RevMan 5.0 was used for Meta-analysis. RESULTS: 13 studies were included, with most of them were of low quality as having high risk of bias. (1) Knowledge index 1: We included 7 'pre-post' studies. Meta-analysis showed that AIDS/HIV prevention and control was effective in promoting the knowledge index 1 among long-distance drivers (RD = 0.15, 95%CI: 0.04 - 0.26). (2) Knowledge index 2: We included 10 pre-post studies, in which the results from Meta-analysis showed that AIDS/HIV prevention and control was effective in promoting the knowledge index 2 among long-distance drivers (RD = 0.24, 95%CI: 0.17 - 0.30). (3) Behavior index: We included 6 pre-post studies in which the results from Meta-analysis showed that AIDS/HIV prevention and control was effective in promoting the behavior index among long-distance drivers (RD = 0.15, 95%CI: 0.10 - 0.19). CONCLUSION: Current evidence demonstrated that HIV/AIDS prevention and control programs were effective.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.032 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".