PP39 Health Technology Assessment And Aging: Moving Evidence To Action
Bibliographic record
Abstract
Introduction: With the rapid increase in technologies and innovations to support a growing aging population in many countries, health technology assessment (HTA) of technologies for the aging populace warrants special consideration. Building on our efforts at Health Technology Assessment international (HTAi) conferences in 2016 and 2017, this presentation will highlight themes generated from two previous HTAi collaborations, with an aim of continuing to build interest and capacity in HTA for aging-related technologies in an international ecosystem that is responsive to local needs and global opportunities. Methods: Researchers from Canada's technology and aging network (AGE-WELL) collaborated with international panelists at HTAi conferences in 2016 and 2017 to explore interest in HTA focused on aging. International panelists shared the current state of aging and HTA in their respective countries. At both sessions, opportunities were provided for participants to rate the importance of themes identified by the panelists. Results: At the 2016 session, the two most highly ranked themes were: (i) how HTA can help identify the unmet needs of older adults in society that could be met by technology; and (ii) engagement of older adults and caregivers. These two themes became the starting point for the panel discussion in 2017. At this session, the highest ranked themes were: (i) identification of challenges in HTA and aging; (ii) approaches to advancing the effectiveness of HTA in addressing technology and aging; and (iii) development of an aging-related interest group in HTAi. Conclusions: International collaborations have identified a number of recommendations to consider for HTA and aging-related work including: developing a good mutual awareness and understanding of barriers and opportunities; the importance of co-creating solutions with patients, healthcare providers, researchers, innovators, and funders; and the identification of a suite of methods and tools that can help accelerate technological innovation in care delivery.
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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 | 0.095 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".