Bibliographic record
Abstract
The FIGO Leadership in Obstetrics and Gynecology for Impact and Change (LOGIC) Initiative in Maternal and Newborn Health was developed on the premise that organizational capacity strengthening in eight low- and middle-income countries would result in improved ability of member associations to take a leadership role in engaging a range of stakeholders in the health sector to discuss evidence and facilitate policy change and clinical practice in maternal and newborn health. Definitions of relevant terms, principles, and a framework for an advocacy plan are presented. The term advocacy is typically not well understood by health professionals, nor generally thought to be part of their role as a clinician, researcher, or educator. "Influence" based on expertise is often more consonant with a clinician's reality, especially where advocacy is perceived as a more political process that may present a barrier in some countries. The organizational capacity development of the FIGO member associations was integral to their ability to exert influence based on evidence, both internally in their associations and with other stakeholders, including the Ministry of Health. Examples of advocacy from each of the eight LOGIC countries are provided, noting that evaluation of impact can be challenging.
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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 | 0.076 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".