Health technology assessment use and dissemination by patient and consumer groups: Why and how?
Bibliographic record
Abstract
OBJECTIVES: Although increasing effort is being devoted to developing strategies to increase knowledge transfer and the uptake of health technology assessment (HTA) by various stakeholders, very little is known about the utilization and dissemination of HTA findings by patient and consumer organizations. The goal of this study is to understand how and why patient and consumer organizations use HTA findings within their organizations, and what factors influence how and when they communicate their findings to members or other organizations. METHODS: We examined the use and dissemination of four controversial HTA reports by sixteen patient and consumer organizations in Ontario and Quebec. We gathered data from semistructured interviews conducted between December 2006 and April 2007. RESULTS: Although HTA findings are often used by the patient and consumer organizations, key differences were observed in exactly how the four HTA reports were used. Three types of use (instrumental, conceptual, and symbolic) are reported and illustrated. We highlight the importance of the organization's mission and knowledge base in explaining the types of use observed. CONCLUSIONS: We contend that the use and dissemination of HTA reports by specific groups could help in widening the debate around controversial health technologies. The implications and opportunities for HTA agencies relate to the following: (i) identification of "lay" organizations that could help in disseminating results; (ii) acknowledgement of a "lay" audience for HTA findings; (iii) strategic inclusion of advocacy groups during the assessment process for highly controversial technologies; and (iv) contribution of these organizations to the push efforts of knowledge transfer.
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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 | 0.056 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".