The state of the art of HTA in mature contexts: the Canadian experience
Bibliographic record
Abstract
The process of Health Technology Assessment (HTA) involves evaluating the value of health technologies. \nThe suitable implementation of HTA is contingent on the particular context in which the assessment is \nconducted, taking into account the stakeholders, stages of the process, techniques, and criteria used for \nevaluation. \nWhile the significance of HTA is widely acknowledged, more literature is required to analyse better the HTA \nprocess, particularly investigating how its principal elements diverge in diverse settings. \nTo bridge this gap, a systematic network analysis of literature was carried out to determine the most \ninvestigated Canadian HTA research streams. \nCanada, ranked in the top ten globally for its public healthcare system and boasting a wealth of health \ntechnology innovations, serves as a mature context where the HTA approach is consistently and effectively \nutilized across healthcare organizations at all levels. \nSeven streams were identified, including macro-HTA, meso-HTA, micro-HTA, ethical considerations, and \npatient involvement. The manuscript brings into the spotlight the fundamental components of the HTA \nprocess involved in each of these streams. \nThe network analysis also uncovers various literature gaps. Future research should investigate how to include \nqualitative and quantitative aspects in HTA and overcome obstacles associated with patients' involvement. \nThis paper provides theoretical and practical contributions by illuminating the organisational structure of the \nHTA approach and delivering guidance to practitioners in seeking more effective implementations of HTA.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".