An Equity Framework for Health Technology Assessments
Bibliographic record
Abstract
Despite the inclusion of equity in the design of many health care systems, pragmatic tools for considering equity systematically, alongside the efficiency categories of cost-effectiveness in health technology assessment (HTA), remain underdeveloped. This article develops a framework to help decision makers supplement the standard efficiency criteria of HTA and avoid building inequities, explicit or implicit, into their methods. The framework is intended as a first step toward creating a checklist for alerting decision makers to a wide range of equity considerations for HTA. This framework is intended be used as part of the process through which advisory bodies receive their terms of reference; scope the agenda prior to the selection of a candidate intervention and its comparators for HTA; prepare background briefing for decision makers; and help to structure the discussion and composition of professional and lay advisory groups during the assessment process. The framework is offered as only a beginning of an ongoing process of deliberation and consultation, through which the matters covered can be expected to become more comprehensive and the record of past decisions and their contexts in any jurisdiction adopting the tool can serve to guide subsequent evidence gathering and decisions. In these ways, it may be hoped that equity will be more systematically and fully considered and implemented in both the procedures and decisions of HTA.
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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 | 0.119 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".