Bibliographic record
Abstract
Chris Rolton grew up on the outskirts of big-city life and first practised medicine just outside London, England. At age 28, however, she said good-bye to the urban hustle and bustle and flew to Goose Bay, Labrador, where she spent 2 years. That experience — and others that have taken Rolton into communities across the country — has been recaptured in her book, Doin' the Locum Motion (Creative Publishers, 2002). During those 35 years Rolton learned that being a locum in Canada means the visiting physician is expected to handle everything — and she means everything. “Fairly regularly I would treat a cat or dog with a fish hook in its mouth,” she says. And she recalls going ice fishing for the first time — and falling in. “Kicking my legs furiously I tried to grab onto the ice,” she writes. “The first time it broke away. The same thing happened the second time. But the third time it held, and I was able to haul my elbows up onto it. Assisted by encouraging yells from my friends, and still kicking like mad, I somehow heaved myself out.” That immersion led to a quick emersion into community life. “The episode caused quite a stir in the community, and patients invariably inquired, ‘Was it you fell in the river, miss?’ ” But she learned much more than how to stay upright on ice, remove fish hooks and stitch up a gash from a harp seal. She discovered that she enjoyed working in small communities and that she could do so without having a permanent practice — that's why she spent the next 35 years filling in for physicians in Newfoundland, Manitoba, and Ontario. “I simply couldn't hack the way most doctors work — 60 patients a day, every day,” says Rolton. “Also, if you're single, there is no point in working a lot. The government just takes your money.” The life of a fill-in physician also came with built-in flexibility, which appealed to her. “I liked the freedom of being able to say no to assignments,” she says. And there was no lack of them, with referrals coming in from physicians, colleagues and even drug company representatives. But there were downsides to this peripatetic life. “You were dealing with patients you don't know and staff you haven't worked with,” says Rolton. And, she adds, she was often reading doctors' notes that she couldn't understand. Still, she contends, “it's much more interesting than seeing the same old faces every day.” Rolton stopped doing the locum motion 11 years ago, when she settled in Carbonear, Nfld. She is actively involved with the local heritage board and may write a second book, a series of stories about the region. Rolton also spends time relaxing with her cat. She's discovered it's nice to have an animal nearby that doesn't have a fishhook in its mouth. — Donalee Moulton, Halifax
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.103 | 0.017 |
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".