Integrating pharmaceutical systems strengthening in the current global health scenario: three ‘uncomfortable truths’
Bibliographic record
Abstract
The response to emergency public health challenges such as HIV, TB, and malaria has been successful in mobilising resources and scaling up treatment for communicable diseases. However, many of the remaining challenges in improving access to and appropriate use of medicines and services require pharmaceutical systems strengthening. Incorporating pharmaceutical systems strengthening into global health programmes requires recognition of a few 'truths'. Systems strengthening is a lengthy and resource-intensive process that requires sustained engagement, which may not align with the short time frame for achieving targets in vertical-oriented programmes. Further, there is a lack of clarity on what key metrics associated with population and patient level outcomes should be tracked for systems strengthening interventions. This can hinder advocacy and communication with decision makers regarding health systems investments. Moving forward, it is important to find ways to balance the inherent tensions between the short-term focus on the efficiency of vertical programmes and broader, longer-term health and development objectives. Global health programme design should also shift away from a narrow view of medicines primarily as an input commodity to a more comprehensive view that recognizes the various structures and processes and their interactions within the broader health system that help ensure access to and appropriate use of medicines and related services.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".