Mobilising modern facts: health technology assessment and the politics of evidence
Bibliographic record
Abstract
Conventional models of 'evidence' for clinical practice focus on the role of randomised controlled clinical trials and systematic reviews as technologies that promote a specific model of rigour and analytic accountability. The assumption that runs through the disciplinary field of health technology assessment (HTA), for example, is that the quantification of evidence about cost and clinical effectiveness is central to rational policy-making and healthcare provision. But what are the conditions in which such knowledge is mediated into decision-making contexts, and how is it understood and used when it gets there? This paper addresses these questions by examining a series of meetings and seminars attended by senior clinical researchers, social care and health service managers in the UK between 1998-2004, and sessions of the House of Commons Health Committee held in 2001 and 2005. These provide contexts in which questions about the value and utility of evidence produced within the frame of HTA were explored in relation to parallel questions about the design, evaluation and implementation of telemedicine and telecare systems. The paper points to the ways that evidence generated in the normative frame of HTA was increasingly seen as one-dimensional and medicalised knowledge that failed to respond to the contingencies of everyday practice in health and social care settings.
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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 | 0.258 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.007 | 0.124 |
| Scholarly communication | 0.034 | 0.061 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.023 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".