Drugs, Health and the Economy: Investment, Innovation, Outcomes, Growth
Bibliographic record
Abstract
Sustainability of the Canadian health system is currently foremost in the minds of many stakeholders. Historically, health expenditures have been viewed as ever increasing and with little visible economic return. Recently, economists have recognized the health arena as an important growth area within the total economy and have begun quantitative analyses of the impact of health investments as drivers of innovation and the general economic advance of nations. In particular, evidence has focused on the discovery and diffusion of new drugs as practical reflections of the quality ladder model of innovation, largely through their provision of improved duration and quality of life and accompanying productivity. The rate of return on innovative drug therapy within the universe of patients who could benefit is, however, impeded by under-prescription of, restricted access to, and/or impaired compliance with, newer efficacious drugs. The authors support further research to assist in future health policy decisions, including wider testing of the partnership/measurement model of disease management as a feasible tool to optimize the social rate of return on already-proven drug therapy. They further recommend these partnerships be designed with enough breadth of vision to facilitate their transition to operational projects compatible with evolving public health policies.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".