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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".