“We have to look deeper into why”: perspectives on problem identification and prioritization of women’s and girls’ health across United Nations agencies
Bibliographic record
Abstract
Eliminating gender inequality and promoting population health are stand-alone goals in the Sustainable Development Goals (SDGs). It is crucial to understand goal setting and policy making processes aimed at promoting gender and health equality given the entrenched and structural natures of these inequalities. Our research examines the process of problem and solution representation, priority setting, and factors that shape the policymaking process concerning women and girls within the UN system in relation to the SDGs. Data for this study were collected from semi-structured one-on-one interviews with participants who have work experience within the United Nations (UN) (n = 9). The analysis was informed by a qualitative descriptive methodology. Our findings identify the role of political forces in influencing policy, the challenges of limited and tied financial resources, the role of scientific evidence and data, and the purpose of different mandates across agencies. Political forces were found to shape the work of UN agencies, often hindering advancement of the SDG agenda. At the same time participants noted how they navigated opposition or what they considered regressive approaches to women and girls' health in order to pursue a more progressive agenda. Finite financial resources were also noted to play an important role in shaping SDG implementation pertaining to women and girls' health. Identification of the types of knowledge, evidence, and data that drive and are given preference in policy creation and development can highlight shortcomings and strengths of current modes of policy development and implementation. Key stakeholders and future research in health and development policy spheres can draw from our findings to gain insight into problem representation and prioritization. This will help identify underlying assumptions that inform work on women's and girls' health and how they shape policy agendas.
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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 | 0.056 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.051 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".