Decision-Making on New Non-Drug Health Technologies (NDTs) by Hospitals and Health Authorities in Canada
Bibliographic record
Abstract
INTRODUCTION: Unlike those for publicly funded drugs in Canada, coverage decision-making processes for non-drug health technologies (NDTs) are not well understood. OBJECTIVES: This paper aims to describe existing NDT decision-making processes in different healthcare organizations across Canada. METHODS: A self-administered survey was used to determine demographic and financial characteristics of organizations, followed by in-depth interviews with senior leadership of consenting organizations to understand the processes for making funding decisions on NDTs. RESULTS: Seventy-three and 48 organizations completed self-administered surveys and telephone interviews, respectively (with 45 participating in both ways). Fifty-five different processes were identified, the majority of which addressed capital equipment. Most involved multidisciplinary committees (with medical and non-medical representation), but the types of information used to inform deliberations varied. Across all processes, decision-making criteria included local considerations such as alignment with organizational priorities. CONCLUSIONS: NDT decision-making processes vary in complexity, depending on characteristics of the healthcare organization and context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".