Building capacity in evidence-based medicine in low-income and middle-income countries: problems and potential solutions
Bibliographic record
Abstract
The early era of evidence-based medicine (EBM) saw the emergence of a cohort of leaders who applied the concepts of clinical expertise, best available evidence and patient preferences to healthcare. Yet, with time, these core components of EBM have become distorted, misinterpreted and hijacked.1 The EBM Manifesto provided a roadmap for tackling the core issues related to the practice and application of EBM.2 One of the important items in the manifesto is to ‘ Encourage the next generation of leaders in evidence-based medicine ’.2 Achieving improvements in healthcare globally requires building and sustaining early and mid-career researchers (EMCRs).3 Yet, there are big gaps in both critical appraisal and research capacity, particularly in low-income and middle-income countries (LMICs), and this hinders development in these regions.4 At the 2019 EBMLive conference (see box 1), we wanted to better understand the problems and challenges that EMCRs encounter. In particular, we focused on EMCRs in those LMICs undergoing major health system transformations, such as Brazil and India. We asked the six recipients of the Building Capacity Bursaries (all co-authors of this commentary) to describe the challenges that they have encountered individually and among their peers, along with potential solutions (see box 2). Their responses reflect healthcare professionals who practice in South America, Africa, the Middle East and Asia. While some challenges are specific to certain settings, we tried to identify, highlight and describe broad overarching themes. Box 1 ### The EBMLive Conference
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.132 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.034 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".