Challenges facing drug utilization research in the Latin American region
Bibliographic record
Abstract
PURPOSE: The International Society of Pharmacoepidemiology (ISPE) in collaboration with the Latin America Drug Utilization Research Group (LatAm DURG), the Medicines Utilization Research in Africa (MURIA) group, and the Uppsala Monitoring Center, is leading an initiative to understand challenges to drug utilization research (DUR) in the Latin American (LatAm) and African regions with the goal of communicating results and proposing solutions to these challenges in four scientific publications. The purpose of this first manuscript is to identify the main challenges associated with DUR in the LatAm region. METHODS: Drug utilization (DU) researchers in the LatAm region voluntarily participated in multiple discussions, contributed with local data and reviewed successive drafts and the final manuscript. Additionally, we carried out a literature review to identify the most relevant publications related to DU studies from the LatAm region. RESULTS: Multiple challenges were identified in the LatAm region for DUR including socioeconomic inequality, access to medical care, complexity of the healthcare system, limited investment in research and development, limited institutional and organization resources, language barriers, limited health education and literacy. Further, there is limited use of local DUR data by decision makers particularly in the identification of emerging health needs coming from social and demographic transitions. CONCLUSIONS: The LatAm region faces challenges to DUR which are inherent in the healthcare and political systems, and potential solutions should target changes to the system.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".