What can implementation science offer civil society in their efforts to drive rights-based health reform?
Bibliographic record
Abstract
Over the years, civil society organizations (CSOs) have made tremendous efforts to ensure that state policies, programmes, and actions facilitate equitable access to healthcare. While CSOs are key actors in the realization of the right to health, a systematic understanding of how CSOs achieve policy change is lacking. Implementation science, a discipline focused on the methods and strategies facilitating the uptake of evidence-based practice and research can bring relevant, untapped methodologies to understand how CSOs drive health reforms. This article argues for the use of evidence-based strategies to enhance civil society action. We hold that implementation science can offer an actionable frame to aid CSOs in deciphering the mechanisms and conditions in which to pursue rights-based actions most effectively. More empirical studies are needed to generate evidence and CSOs have already indicated the need for more data-driven solutions to empower activists to hold policymakers to account. Although implementation science may not resolve all the challenges CSOs face, its frameworks and approaches can provide an innovative way for organizations to chart out a course for reform.
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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 | 0.347 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.046 | 0.069 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.029 | 0.030 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".