Bibliographic record
Abstract
A few months ago I wrote about some rural communities that went online in search of physicians (CMAJ 2000; 162[3]:401). Now an organization that represents Ontario‚s newest doctors has taken things a step further. The Professional Association of Internes and Residents of Ontario (PAIRO) represents about 2400 physicians-in-training. Its new Web site (www.pairoregistry.com) is an online matchmaking service for physicians and the underserviced communities that are dying to recruit them. And it is yet another sign of the ongoing crisis in rural/remote medicine. This Web portal brings doctors and communities together under a virtual roof: it allows communities to court doctors online and physicians to search for desirable places to work by spelling out their professional criteria. ”We‚re offering the system at no cost to all Ontario communities with physician shortages because we think it can be an integral link in helping them recruit and retain the physicians they need,” says Dr. Joshua Tepper, PAIRO‚s president. The site offers 2 distinct paths. Physicians can use its search capabilities to look for communities that fit their individual preferences. They can search by community name, region, specialty and community size. Once a list is compiled the system lets searchers move to individual community Web pages, where they can find detailed information about the towns and cities they have identified. An email link is usually available, so contact can be made almost immediately. From the other side, the registry lets communities build a customized set of Web pages extolling their virtues. The communities provide the information and get a free Web site and listing in the PAIRO registry. All the programming is done online through an automated system, so anyone can use it. The system went live only in May, so it is too early to tell how successful it will be. However, Tepper said an earlier version of the site had more than 30 000 page views in 1999. —
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.227 | 0.149 |
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".