Dissemination of health technology assessments: identifying the visions guiding an evolving policy innovation
Bibliographic record
Abstract
Abstract—Health technology assessment (HTA) has received increasing support over the past twenty years in both North America and Europe. The justifi cation for this fi eld of policy-oriented research is that evidence about the effi cacy, safety, and cost-effectiveness of technology should contribute to decision and policy making. However, concerns about the ability of HTA producers to increase the use of their fi ndings by decision makers have been expressed. Although HTA practitioners have recognized that dissemination activities need to be intensifi ed, why and how particu-lar approaches should be adopted is still under debate. Using an institutional theory perspective, this article examines HTA as a means of implementing knowledge-based change within health care systems. It presents the results of a case study on the dis-semination strategies of six Canadian HTA agencies. Chief executive offi cers and executives (n = 11), evaluators (n = 19), and communications staff (n = 10) from these agencies were interviewed. Our results indicate that the target audience of HTA is frequently limited to policy makers, that three confl icting visions of HTA dissemina-tion coexist, that active dissemination strategies have only occasionally been applied, and that little attention has been paid to the management of diverging views about the value of health technology. Our discussion explores the strengths, limitations, and trade-offs associated with the three visions. Further efforts should be deployed within
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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 | 0.201 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.042 | 0.027 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".