The Subjectivity of Objectivity: The Social, Cultural and Political Shaping of Evidence-Based Medicine
Bibliographic record
Abstract
Most critiques of evidence-based medicine (EBM) focus on the scientific shortcomings of the technique. Social scientists are more likely to criticize EBM for it ideological biases. In this chapter, we provide a different critique. Our critique does not focus on the inconsistencies and practical problems of data collection. We find EBM to be flawed, not because it fails to be scientific, but because - like all science - it imports the biases of researchers and clinicians. In this paper we explore three sources of these biases: the structural arrangement of clinical research, the cultural ideas that shape research questions and research design, and the political goals that influence how evidence is presented in the public arena. Our evidence - drawn from separate studies of 1) the funding of pharmaceutical research, 2) the use of research data to support government policy on home birth in the Netherlands, and 3) the use of previous research on adverse events in The Institute of Medicine's report on Medical Error - shows how bias finds its way into the scientific literature. If medicine wants to be truly evidence based, it has to take these structural, cultural and political biases into account when designing policies and practices.
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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 | 0.387 | 0.429 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.016 | 0.266 |
| Scholarly communication | 0.036 | 0.035 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.018 | 0.028 |
| 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; 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".