Building capacities of elected national representatives to interpret and to use evidence for health-related policy decisions: A case study from Botswana
Bibliographic record
Abstract
Elected national representatives make decisions to fund health programmes, but may lack skills to interpret evidence on health-related topics. In 2011, we surveyed the 61 members of Botswana's Parliament about their use of epidemiological evidence, then provided two half-days of training about using evidence. We included the importance of counter-factual evidence, the number needed to treat, and unit costs of interventions. A further session in 2012 covered evidence about the HIV epidemic in Botswana and planning the best mix of interventions to reduce new HIV infections. The 27 respondents reported they lacked good quality, timely evidence, and had difficulty interpreting and using evidence. Thirty-six, including seven ministers, attended one or both trainings. They participated actively and their evaluation was positive. Our experience in Botswana could potentially be extended to other countries in the region to support evidence-based efforts to tackle the HIV epidemic.
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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 | 0.023 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".