From research to a political commitment to strengthen access to surgical, obstetric, and anesthesia care in Africa by 2030
Bibliographic record
Abstract
Objective: This study aimed to engage African leaders and key stakeholders to commit themselves toward the strengthening of surgical, obstetric, and anesthesia care systems by 2030 in Africa. Methods: From research to a political commitment, a baseline assessment was performed to foster the identification of the gaps in surgical care as a first step of an inclusive process. The preliminary findings were discussed during the International Symposium on Surgical, Obstetric, and Anesthesia Systems Strengthening by 2030 in Africa. The conclusions served to draft the Dakar Declaration and its Regional Action Plan 2022-2030 to improve access to surgical care by 2030 in Africa, endorsed by Heads of State. Results: The International Symposium was composed of two meetings that gathered (i) 85 scientific experts and (ii) 28 ministers of health or representatives from 28 sub-Saharan African countries. The 28 African countries represent (i) 51% of the continent's total population, (ii) 68% of the 47 African countries of the WHO Africa Region, (iii) 58% of all African Union countries, and (vi) 79% (3,371) of the WHO Africa Region's total (4,271) health districts. The International Symposium and the Heads of State Summit successfully produced the Dakar Declaration on access to equitable, affordable, and quality Surgical, Obstetric, and Anesthesia Care by 2030 in Africa and its Regional Actions Plan 2022-2030 which prioritizes 12 urgent actions needed to be implemented, six strategic priorities, 16 key indicators, and an annual dashboard to monitor progress. Conclusion: " Agenda 2063.
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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 | 0.122 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".