Bibliographic record
Abstract
According to the College of Physicians and Surgeons, Canada’s regulatory body for physicians in Ontario, the doctor shortage in Ontario has reached “unprecedented proportions ” (London Free Press, 2004). Predictions indicate that by the end of this decade we may be short 6000 doctors, resulting in 2 million people being left without a physician (Association of International Physicians & Surgeons of Ontario, 2002). In fact, reports of doctor shortages across North America are becoming more and more commonplace, and various governing bodies continue to search for solutions to this problem. In order to recommend such solutions, the causes of the problem must first be understood. In the U.S., as well as Canada, the area of medicine being hardest hit by these shortages is that of primary care. Medical graduates here in North America are choosing to enter areas of specialty due to the greater income potential and career options such positions offer (Brigham & Women’s Hospital, 2004). The resulting decrease in the number of graduates entering into a general practice raises concern. Add to this issue the fact that Canada is an aging population, and we can see that we will be experiencing an even greater shortage due to the number of our current physicians quickly approaching retirement age.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.852 | 0.752 |
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; the direct Gemma label and the distilled Codex classifier 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".