Measuring the Outcomes of a Comprehensive HIV Care Course
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of the Infectious Diseases Institute's 4-week course for African doctors on comprehensive management of HIV including antiretroviral therapy on four outcomes: (1) clinical skills, (2) clinical activities, (3) monitoring of HIV patients, and (4) training activities DESIGN: Clinical exam at beginning and end of course and at follow-up 3 to 4 months later, and a cross-section telephone survey. METHODS: Forty-seven doctors attending the course (October 2004, November 2004, March 2005, and April 2005) agreed to participate. A 17-item Clinical Exam Checklist was used to assess clinical skills. A telephone survey was conducted 1 month after the course to collect data in four areas: clinical activities, monitoring of HIV patients, case studies on initiation of ART, and training activities. RESULTS: The course improved the clinical skills of doctors. Between the beginning and end of the course, their clinical skills improved significantly in 11 of 17 areas (n = 34). Between the end of the course and follow-up, their skills improved significantly in three areas (n = 14). The trainees were practicing HIV care and training. The telephone survey (n = 46) showed that 93% of trainees treated HIV patients, 35% provided training on HIV, and 47% monitored the weight of the last HIV patient treated (patient's weight was a clinical end point to measure health status). At follow-up, everyone provided training and trained an average of 20 people per month.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".