On being heckled at a National Health Technology Conference: Patient participation and democratic discourse
Bibliographic record
Abstract
This article uses my experience of being heckled by patient advocates at a health technology conference in Canada as a springboard for discussing the politics of health technology assessment (HTA). While HTA is widely understood and practised as a scientific endeavour grounded in rigorous quantitative research methods, the socio-political aspects of HTA cannot be separated from the scientific. Integrating the social, political, and ethical dimensions of HTA into the practice of assessment means understanding how a technology will shift power relationships among actors, alter resource flows, and affect how knowledge is produced and circulated. I suggest these factors contributed to the hostile reception I received when I attempted to present a paper about the biased selection of patient advocates involved in Canada's main HTA agency. As India embarks on the challenge of establishing its own agency to support healthcare decision-making, and as patient advocacy groups rise in India with the support of the pharmaceutical industry, I offer this account as a cautionary tale to those shaping India's new agency.
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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 | 0.051 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.067 | 0.078 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.016 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 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".