Landmark Articles From Volumes 31-40 / Des articles-jalons tirés des volumes 31 à 40 - Evaluation of the AIDS Prevention Street Nurse Program: One Step at a Time
Bibliographic record
Abstract
The AIDS Prevention Street Nurse Program uses specially prepared community health nurses to focus on HIV and STD prevention with marginalized, hard-to-reach and high-risk clients within a broader context of harm reduction and health promotion. Street nurses (n = 17), service providers (n = 30), representatives of other HIV/STD programs in the province of British Columbia, Canada (n = 5), and clients (n = 32) were interviewed during an evaluation for the purpose of describing the nurses' work, the challenges the nurses' face, the fit of the program with other services, and the impact of the nurses' work. This article describes the impact of the nurses' work on clients. Impact/outcome changes reflected a progression from knowledge to behavioural levels and to major indicators of health/illness. Impact on clients included: knowing more about HIV/AIDS, their own situation, and options; receiving essential supplies to reduce harm and promote health; changing behaviour to reduce disease transmission, improve resistance, and promote health; connecting with help; feeling better about themselves and others; feeling supported; influencing others; receiving earlier attention for problems; being healthier with or without HIV; making major changes in drug use; and likely decreasing morbidity and mortality. The program was found to be clearly effective in making a positive impact on clients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.043 |
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".