How Good Is Good Enough? Standards in Policy Decisions to Cover New Health Technologies
Bibliographic record
Abstract
Health technology coverage decisions require reasonable criteria, for example, the requirement that a technology be effective, efficient, legitimate in purpose, acceptable in its effects, safe and so on. The leap from such criteria to decisions requires not only evidence, but also standards. Decision-makers must specify their values, which apply in general, regarding what is "good enough" before they can judge any technology in particular. This paper will do the following: (1) describe the key analytic tasks involved in defining coverage criteria and their standards, (2) identify some of the policy applications of explicit standards to coverage decisions and (3) review the policy uses of such standards, including some challenges they pose. The problem of identifying cost-effectiveness standards will be used to illustrate key issues. It is argued that a precedent-based understanding of standards is relevant in the Canadian policy context, where fairness is crucial. Studies of actual decision-making that seek standards inductively have been misguided in their focus on central tendencies to the neglect of outliers (precedents), while deductive analyses and rules of thumb have been ungrounded in prevailing values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.146 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.021 | 0.038 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".