AGING AND HEALTH TECHNOLOGY ASSESSMENT: AN IDEA WHOSE TIME HAS COME
Bibliographic record
Abstract
OBJECTIVES: With the increase in technologies to support an aging population, health technology assessment (HTA) of aging-related technologies warrants special consideration. At Health Technology Assessment international (HTAi) 2016 and HTAi 2017, an international panel explored interests in HTA focused on aging. METHODS: Panelists from five countries shared the state of aging and HTA in their countries. Opportunities were provided for participants to discuss and rate the themes identified by the panelists. RESULTS: In 2016, the highest ranked themes were: (i) identifying unmet needs of older adults that could be met by technology-how can HTA help?; (ii) differences in assessment of aging-related technologies-what is the scope?; and (iii) involvement of older adults and caregivers. These themes became the starting point for discussion in 2017, for which the highest ranked themes were: (i) identification of challenges in HTA and aging; and (ii) approaches to advancing effectiveness of HTA for aging. CONCLUSION: These discussions allowed for examination of future directions for HTA and aging: engagement of older adults to inform the agenda of HTA and the broader public policy enterprise; a systems approach to thinking about needs of older persons should support the type and level of care desired by the individual rather than the health institutions, and HTA should reflect these desires when evaluating technological aides; and there is potential for health information systems and "big data" to support HTA activities that assess usability of technologies for older adults. We hope to build on the momentum of this community to continue exploring opportunities for aging and 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 | 0.074 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.025 | 0.076 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.023 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".