Evidence-Based Medicine: Opportunities and Challenges in a Diverse Society
Bibliographic record
Abstract
In this article we explore the discourse and practice of evidence-based medicine (EBM) in the context of social and cultural diversity. The article consists of 2 parts. First, we begin by defining EBM, describing its historical development and current ascendance in medical practice. We then note its importance in contemporary psychiatry, comparing dynamics between the United States and Canada. Secondly, we offer a constructive critique of the application of EBM and evidence-based practices in the context of ethnocultural diversity, as one consistent reflection on the EBM literature is that it is does not adequately address issues of diversity. In doing so, we use the situation here in Canada as an extended case study, though our observations will likely be applicable in other diverse nations, such as the United States, the United Kingdom, and Australia. We critically examine the following 6 issues related to the practice of EBM in a diverse society: generalizability and transferability of evidence-based interventions; diversifying standards of evidence in EBM; strategies to address diversity in EBM research; cultural adaptations of evidence-based interventions; integrating idiographic knowledge; and, training and health service delivery. Concurrent with our critique, we offer research and practice suggestions that may address outstanding challenges vis-à-vis the practice of EBM in a diverse society. These include a need for more effectiveness research, more openness to diverse sources of knowledge, better integration of idiographic and nomothetic knowledge, and a critical approach to extrapolation and transfer of knowledge.
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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 | 0.188 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.035 | 0.115 |
| Scholarly communication | 0.042 | 0.049 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.024 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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".