The Role of Health Professional Associations in the Promotion of Global Women's Health
Bibliographic record
Abstract
Health professional associations, especially those from countries with the highest maternal death burden, have vital roles to play in improving maternal and newborn health and in achieving the Millennium Development Goals 4 and 5. Possessing the knowledge, skills, and influence to positively impact practice at the service delivery level, they can also advocate for change at the policy level and lobby for higher priority and greater investment in the maternal and newborn health field at the national level. The ability of professional associations to assume this leadership is nevertheless contingent on their institutional capacities to achieve planned goals and objectives in support of their organizational mission and strategic priorities. Since 1998, the Society of Obstetricians and Gynaecologists of Canada (SOGC) has been supporting the capacity development efforts of peer professional associations in low-resource countries. SOGC's work in this specific area has led it to develop and pilot an Organization Capacity Improvement Framework (OCIF) that guides professional associations, incrementally, in successive cycles of capacity development. Building on capacity developed within previous capacity-building cycles, this article summarizes and reports on the recent outcomes of the Asociación de Gynecoloígia y Obstetricia de Guatemala's (AGOG) organizational development efforts and the impact they have had in positioning the association as an important contributor in national efforts to improve maternal and newborn health outcomes in the country.
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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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".