HEALTH TECHNOLOGY ASSESSMENT, RESEARCH, AND IMPLEMENTATION WITHIN A HEALTH REGION IN ALBERTA, CANADA
Bibliographic record
Abstract
OBJECTIVES: To determine the need for and implement health technology assessment (HTA) to inform decision making and policy within a regional health care system in Calgary (Alberta, Canada). METHODS: Published literature and organizational materials for the Calgary Health Region (CHR) and HTA units worldwide were reviewed. Key individuals within the provincial health ministry (Alberta Health and Wellness), CHR, the University of Calgary (U of C), funding agencies, and HTA organizations were consulted in a structured fashion. A structure for a regional HTA program was developed, taking into account relationships between these organizations. RESULTS: A locally focused HTA and implementation unit was deemed desirable. The Calgary Health Technology Implementation Unit (CaHTIU) was established. The CaHTIU was designed to efficiently integrate with CHR planning as well as undertake independent research activities. HTA activities focus primarily on CHR needs and are managed by a Health Technology Advisory Committee (HTAC) that consists of CHR management and other key individuals. Working groups contribute to and coordinate HTAs and implementation under the leadership of the unit Director, and include content as well as management individuals. The unit cooperates where appropriate with extant Canadian HTA organizations. CONCLUSIONS: The Calgary HTA unit is unique in Canada, because it functions within a regional health care system as well as a research institution. Advantages include a local focus in terms of applied HTAs, a systematic process for implementation of recommendations, and a collaborative atmosphere for research within the U of C.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".