ASSESSING THE IMPACTS OF CITIZEN DELIBERATIONS ON THE HEALTH TECHNOLOGY PROCESS
Bibliographic record
Abstract
OBJECTIVES: We assessed the impacts of a Citizens' Reference Panel on the deliberations of a provincial health technology advisory committee and its secretariat, which produce, recommendations for the use of health technologies in Ontario, Canada. METHODS: A fourteen-member citizens' reference panel was convened five times between February 2009 and May 2010 to participate in informed, facilitated discussions to inform the assessment of individual technologies and provincial health technology assessment processes more generally. Qualitative data collection methods were used to document observed and perceived impacts of the citizens' panel on the health technology assessment (HTA) process. RESULTS: Panel impacts were observed for all technologies reviewed, at two different stages in the HTA process, and represented macro- (raising awareness) and micro-level (informing recommendations) impacts. These impacts were shaped by periodic opportunities for direct and brokered exchange between the Panel and the expert advisory committee to clarify roles, foster accountability, and build trust. Our findings offer new insights about one of the main considerations in the design of deliberative participatory structures-how to maintain the independence of a citizens' panel while ensuring that their input is considered at key junctures in the HTA process. CONCLUSIONS: Citizens' panels can exert various impacts on the HTA process. Ensuring these types of structures include opportunities for direct exchange between citizens and experts, to clarify roles, promote accountability, and build trust will facilitate their impacts in a variety of 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 | 0.233 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".