Bridging evidence, policy, and practice to strengthen health systems for improved maternal and newborn health in Pakistan
Bibliographic record
Abstract
Policy and decision making should be based on evidence, but translating evidence into policy and practice is often sporadic and slow. It is recognised that the relationship between research and policy uptake is complex and that dissemination of research findings is necessary, but insufficient, for policy uptake. Political, social, and economic context, use of (credible) data and dialogues between and across networks of researchers and policymakers play important roles in evidence uptake. Advocacy is the process of mobilising political and public opinions to achieve specific aims and its role is crucial in mobilising key actors to push for policy uptake. Advocacy and research groups (i.e. those who would like to see research evidence used by policymakers) may use different approaches and tools to stimulate the diffusion of research findings. The use of mass- and social media, communication with study participants, and the involvement of stakeholders at the early stages of research development are examples of the approaches that can be employed to stimulate diffusion of evidence and increase evidence uptake. The Research and Advocacy Fund (RAF) for Maternal and Newborn Health (MNH) worked within the health system context in Pakistan with the aim of espousing the principles of evidence, advocacy, and dissemination to improve MNH outcomes. The articles included in this special issue are outputs of RAF and highlight where RAF's approaches contributed to MNH policy reforms. The papers discuss critical health system issues facing Pakistan, including service delivery components, demand creation, equitable access, transportation interventions for improved referrals, availability of medicines and equipment, and health workforce needs. In addition to these tangible elements, the health system 'software', i.e. the power and the political and social contexts, is also represented in the collection. These articles highlight three considerations for the future: the growing importance of implementation research, the crucial need for participation and ownership, and the recognition that policymaking can be 'informed' by rather than 'based-on' evidence. The future challenge will be to continue the momentum RAF has created and to welcome a new era of health, wealth, and growth for Pakistan.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".