Human resources for the approval of new drugs in Canada, Australia, Sweden, the United Kingdom and the United States.
Bibliographic record
Abstract
The time required to approve new drugs in Canada is significantly longer than that in Sweden, the United Kingdom and the United States. The timeliness with which a regulatory agency approves drugs may be influenced by the human resources available to review applications. Therefore, the number of full-time equivalent (FTE) staff members who evaluate and approve new drug applications was sought directly from the regulatory agencies of Canada, Australia, Sweden, the United Kingdom and the United States. Information was received from the Therapeutic Products Directorate (TPD) of Health Canada, the Swedish Medical Products Agency (MPA), the United Kingdom Medicines Control Agency (MCA) and the United States Food and Drug Administration (FDA). The Australian Therapeutic Goods Administration (TGA) did not provide data, but the Australian Pharmaceutical Manufacturers Association estimated the number of personnel reviewing drug applications at the TGA to be 102. After adjustment to eliminate staff members whose primary responsibility is reviewing generic applications, there were an estimated 159 FTE staff members at the TPD, 1610 at the FDA, an estimated 76 at the TGA, 60 at the MCA and 46 at the MPA. Thus, the number of personnel in Canada is two to 3.5 times that in Australia, the United Kingdom and Sweden, but less than 10% of that in the United States. Because Sweden, the United Kingdom and the United States all have significantly shorter review and approval times than Australia and Canada, the number of review staff does not appear to be a direct major determinant of the timeliness of an agency's review and approval performance.
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.030 |
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".