The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat): Results From a Skills Workshop
Bibliographic record
Abstract
The Vanderbilt-Nigeria Biostatistics Training Program (VN-BioStat) aims to establish a research and training platform for biostatisticians doing HIV-related research in Nigeria, including enhancing mid-level biostatistics capacity through annual workshops. This paper describes findings from the inaugural workshop in Kano, Nigeria. Participants were surveyed before and after the workshop to assess their self-perceived familiarity with and confidence in their abilities to use statistical software and apply specific statistical techniques, as well as to gather feedback regarding the conduct of the workshop and future topic areas. Of the 23 participants enrolled in the workshop, 22 (96%) completed both pre- and post-workshop assessments. In both pre-workshop and post-workshop surveys, participants ranked their confidence in statistical skills using Likert scales. Scores were transformed to a 0-100 scale, and averages computed. Participants also shared open-ended feedback about the workshop and suggested future topic areas. Before the training, the average participant reported having either a "beginner" (30% of participants) or "moderate" (43%) level of familiarity with R. Many participants (65%) rated themselves as having "moderate" or "expert" familiarity with SPSS. Pre-workshop averages for confidence ranged from 26 to 64, with lowest confidence in "expanding continuous covariates in regression models and interpret results" and highest confidence in "fitting and interpreting results from a linear regression model". Post-workshop averages for confidence were all above 70. The lowest post-workshop score (74) was for "fit and interpret results from a semiparametric linear transformation model". The greatest increase in confidence was observed in "expanding continuous covariates in regression models using splines and interpreting results" and the lowest increase was in "fitting and interpreting results from a linear regression model." Participants offered positive feedback on instructor effectiveness (4.9/5) and overall course quality (4.9/5). While the overall course was rated on a 0-100 scale as "moderately difficult" (mean ± SD: 40.5 ± 17.5), the participants felt the course was highly organized (87.7 ± 17.8), and the information was moderately easy to learn (81.9 ± 15.9). Suggestions for future workshops included providing supplementary resources for out-of-classroom learning and releasing codes in advance to enhance participants' preparation. Among suggestions for future workshop topics, 80% of respondents listed survival analysis. Lessons learned provide insight into how short-term training opportunities can be leveraged to build biostatistics capacity in similar settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".