Health technology assessment and public health: a time for convergence
Bibliographic record
Abstract
Health Technology Assessment (HTA) and Public Health have more in common than meets the eye. Despite having distinct historical trajectories and organizational structures, these fields of applied research share several key defining features: the interdisciplinary nature of their core activities and required expertise; the use of a variety of methods to generate and synthesize evidence; and their enhanced focus on knowledge translation. In light of unprecedented technological innovation, population aging and economic concerns, HTA and Public Health also face the same difficult questions. How to prioritize interventions aimed at preventing, diagnosing and treating chronic diseases? How to account for the social, ethical and legal implications of increasingly expensive and complex interventions? What methodologies should be used to evaluate these interventions? How best to use available evidence when randomization is neither possible nor desirable? Fuelled by the evidenced-based paradigm … Correspondence: Renaldo N. Battista, Department of Health Administration, University of Montreal, C.P. 6128 succursale Centre-Ville, Montreal H3C 3J7, Quebec, Canada, e-mail: renaldo.battista{at}umontreal.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.246 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".