How can health technology assessment support our response to public health emergencies?
Bibliographic record
Abstract
Public health emergencies (PHEs), such as the COVID-19 crisis, are threats to global health and public order. We recommend that countries bolster their PHE responses by investing in health technology assessment (HTA), defined as a systematic process of gathering pertinent information on and evaluating health technologies from a medical, economic, social and ethical standpoint. We present examples of how HTA organizations in low- and middle-income countries have adapted to supporting PHE-related decisions during COVID-19 and describe the ways HTA can help the response to a PHE. In turn, we advocate for HTA capacity to be further developed globally and for increased institutional acceptance of these methods as a building block for preparedness and response to future PHEs. Finally, the long-term potential of HTA in strengthening health systems and embedding confidence and transparency into scientific policy should be recognized.
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How this classification was reachedexpand
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 | 0.244 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".