How Good Is Good Enough? Standards in Policy Decisions to Cover New Health Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it