The Introduction of New Non-Drug Health Technologies (NDTs) into Canadian Healthcare Institutions: Opportunities and Challenges
Bibliographic record
Abstract
INTRODUCTION: A recent pan-Canadian survey of 48 health organizations concluded that structures, processes, factors and information used to support funding decisions on new non-drug health technologies (NDTs) vary within and across jurisdictions in 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 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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".