Assessing national medicines policies: a protocol for systematic review of access to medicine assessment metrics and indicators
Bibliographic record
Abstract
INTRODUCTION: Efforts to improve access to essential medicines globally include the implementation and assessment of national medicine policies. Although existing assessment tools may identify gaps, they do not link these to health system functions and policy priority areas in a way that facilitates decision-making. This systematic review aims to identify and map out existing indicators assessing essential medicines policies; assess their strengths and limitations and understand the parameters required to develop a new instrument for assessing access to medicines in a way that better supports decision makers in identifying and addressing the gaps in their national medicine policies. METHODS AND ANALYSIS: This systematic review is guided by the updated Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol. Eligible studies will be identified across scholarly databases (MEDLINE, Embase and Scopus) and grey literature using relevant subject headings and text words. Title, abstract screening and full-text screening will be conducted by two independent reviewers. Indicators or assessment tools focused on the performance of national medicine policies with respect to improving access to essential medicines will be eligible. Data will be extracted using a pretested data abstraction form. Findings will be reported in the form of a descriptive narrative. This study will provide insights into the relevance of existing indicators and tools for assessing access to essential medicines policies. This review will also discuss the opportunities for the development of a new instrument for the assessment of national medicines policies in a way that better supports decision makers to improve access to essential medicines. ETHICS AND DISSEMINATION: This systematic review does not require ethics approval as it only aims to synthesise evidence from the literature. Findings will be disseminated to key health stakeholders including researchers and global and public health organisations and published in a peer-review journal. This protocol has been registered on PROSPERO (CRD42023493052).
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.
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 | 0.220 | 0.253 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".