Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,103 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».