The most important facilitators and barriers to the use of Health Technology Assessment in Canada: a best–worst scaling approach
Bibliographic record
Abstract
BACKGROUND: Health Technology Assessment (HTA), which can support public drug reimbursement decisions will play a core function in the planned national Pharmacare program in Canada. To address existing barriers to the use of HTA, these must be ranked in order of priority. The goal of this study was to access the relative importance of known facilitators and barriers to the use of HTA in the context of the Canadian health care system, with attention to differences between regions and stakeholder groups. METHODS: We used the best-worst scaling object case approach to elicit a quantitative ranking of a list of 20 facilitators and 22 barriers. A sample of 68 Canadian HTA stakeholders, including members of expert committees, decision/policymakers, researchers/academics, and others participated in the study. Their task was to identify the most important and the least important item in 12 sub-sets of five facilitators and 14 sub-sets of five barriers. FINDINGS: Relative Importance Scores derived via hierarchical Bayes analysis revealed relations, engagement, and contact between stakeholders as most important on both the barrier and facilitator sides. Other top-ranked facilitators included the availably of credible and relevant research. Other top-ranked barriers included inconsistencies in the evidence and limited generalizability. The availability of HTA guidelines did not rank highly on either side. The main limitation of the study was the challenge with reaching the relevant respondents; this was mitigated by involving the national HTA agency in the research. CONCLUSION: Canadian stakeholders consider the relationships within the HTA network among the most important. Policies should focus on strengthening these relationships. Future research should focus on the connectivity and distribution of knowledge and power within the HTA network.
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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 | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".