First Africa non-communicable disease research conference 2017: sharing evidence and identifying research priorities
Bibliographic record
Abstract
Non-communicable diseases (NCDs) prevalence is rising fastest in lower income settings, and with more devastating outcomes compared to High Income Countries (HICs). While evidence is consistent on the growing health and economic consequences of NCDs in sub-Saharan Africa (SSA), specific efforts aimed at addressing NCD prevention and control remain less than optimum and country level progress of implementing evidence backed cost-effective NCD prevention approaches such as tobacco taxation and restrictions on marketing of unhealthy food and drinks is slow. Similarly, increasing interest to employ multi-sectoral approaches (MSA) in NCD prevention and policy is impeded by scarce knowledge on the mechanisms of MSA application in NCD prevention, their coordination, and potential successes in SSA. In recognition of the above gaps in NCD programming and interventions in Africa, the East Africa NCD alliance (EANCDA) in partnership with the African Population and Health Research Center (APHRC) organized a three-day NCDs conference in Nairobi. The conference entitled "First Africa Non-Communicable Disease Research Conference 2017: Sharing Evidence and Identifying Research Priorities" drew more than one hundred fifty participants and researchers from several institutions in Kenya, South Africa, Nigeria, Cameroon, Uganda, Tanzania, Rwanda, Burundi, Malawi, Belgium, USA and Canada. The sections that follow provide detailed overview of the conference, its objectives, a summary of the proceedings and recommendations on the African NCD research agenda to address NCD prevention efforts in Africa.
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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 | 0.308 | 0.309 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.037 | 0.024 |
| Open science | 0.011 | 0.051 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".