Developing a How-to-Guide for Health Technology Reassessment: "The HTR Playbook"
Bibliographic record
Abstract
Background: To develop a knowledge translation (KT) tool that will provide guidance to stakeholders actively planning or considering implementation of a health technology reassessment (HTR) initiative. Methods: The KT tool is an international and collaborative endeavour between HTR researchers in Canada, Australia, and the United Kingdom. Evidence from a meta-review of documented international HTR experiences and approaches provided the conceptual framing for the KT tool. The purpose, audience, format, and overall scope and content of the tool were established through iterative discussions and consensus. An initial version of the KT tool was beta-tested with an international community of relevant stakeholders (i.e., potential users) at the Health Technology Assessment International 2018 annual meeting. Results: An open access workbook, referred to as the HTR playbook, was developed. As a KT tool, the HTR playbook is intended to simplify the complex HTR planning process by navigating users step-by-step through 6 strategic domains: characteristics of the candidate health technology (The Stats and Projections), stakeholders to engage (The Team), potential facilitators and/or barriers within the policy context (The Playing Field), strategic use of different levers and tools (The Offensive Plays), unintended consequences (The Defensive Plays), and metrics and methods for monitoring and evaluation (Winning the Game). Conclusion: The HTR playbook is intended to enhance a user’s ability to successfully complete a HTR by helping them systematically consider the different elements and approaches to achieve the right care for the patient population in question.
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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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.022 |
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".