Assessing the performance of health technology assessment organizations: A framework
Bibliographic record
Abstract
In light of growing demands for public accountability, the broadening scope of health technology assessment organizations (HTAOs) activities and their increasing role in decision-making underscore the importance for them to demonstrate their performance. Based on Parson's social action theory, we propose a conceptual model that includes four functions an organization needs to balance to perform well: (i) goal attainment, (ii) production, (iii) adaptation to the environment, and (iv) culture and values maintenance. From a review of the HTA literature, we identify specific dimensions pertaining to the four functions and show how they relate to performance. We compare our model with evaluations reported in the scientific and gray literature to confirm its capacity to accommodate various evaluation designs, contexts of evaluation, and organizational models and perspectives. Our findings reveal the dimensions of performance most often assessed and other important ones that, hitherto, remain unexplored. The model provides a flexible and theoretically grounded tool to assess the performance of HTAOs.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".