What can implementation science offer civil society in their efforts to drive rights-based health reform?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it