Bibliographic record
Abstract
The recent article by Barber, et al presents important data on the Canadian rheumatology workforce and the quantitative shortcomings that require remediation 1 . I wish to make a small correction. The authors state on page 255 of their article "to our knowledge, ours is the first national rheumatology workforce survey in Canada." There actually was a previous survey by the Royal College of Physicians and Surgeons of Canada and the Federal Department of National Health and Welfare 2 . That survey showed that in 1975 there was 1 rheumatologist per 180,000 population. The report expressed the wish for a ratio of 1:118,200 to be achieved at some unspecified time in the future. In 2017, we are better off than we were in 1975, although the advance has occurred at a less than dizzying pace. It remains to be seen how the 10 provincial and 3 territorial healthcare systems (not to mention the federal one) will address the challenges identified in 2017 for more rheumatologists and a better distribution. The prospects of early success are not rosy in a country that still ranks low in access to healthcare 3 .
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".