LOCAL HEALTH TECHNOLOGY ASSESSMENT IN CANADA: CURRENT STATE AND NEXT STEPS
Bibliographic record
Abstract
OBJECTIVES: Canada has witnessed expansion of the health technology assessment (HTA) infrastructure in the last 25 years. Local HTA entities at the hospital or regional level are emerging to assist decision makers in the acquisition, implementation, maintenance, and disinvestment of healthcare technologies. There is a need to facilitate collaboration and exchange of expertise and knowledge between these entities regarding the role of local HTA in Canada. METHODS: In November 2013, the pan-Canadian Collaborative hosted a symposium, Hospital/Regional HTA: Local Evidence-based Decisions for Health Care Sustainability, bringing together over 60 HTA producers, researchers, stakeholders, and manufacturers involved in local HTA across Canada. The objective was to showcase the diversity of local HTA in Canada, while highlighting common gaps to be addressed. RESULTS: The Symposium focused on current practices in local HTA in Canada to support informed decision making, and opportunities for information sharing and provide equal access to timely evidence-based information to decision makers. The main themes included assessment of evidence for local HTA, contextualization, stakeholder engagement in local HTA, knowledge translation and impact of recommendations, and challenges and opportunities for local HTA. CONCLUSIONS: Local HTA in Canada complements HTAs conducted at the provincial and federal levels to improve the efficient and effective health service delivery in institutions or regions faced with limited resources. Some challenges faced by local HTA producers to influence hospital policies and clinical practice involve the engagement of healthcare professionals and potential lack of training and support necessary for the introduction of a new technology.
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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 | 0.078 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".