Notice bibliographique
Résumé
When Dr. Karim Qayumi arrived in Canada in 1983 after fleeing Soviet-occupied Afghanistan with his wife and young son, he and his wife practically lived at the local library for 6 months teaching themselves English. Then he rolled up his sleeves and visited the University of British Columbia. Today Qayumi, a professor of cardiovascular and thoracic surgery at UBC, is prime mover behind the creation of a high-tech Centre of Surgical Excellence at the Vancouver Hospital. He says it will give students “dry lab” experience with new technologies, while at the same time educating residents and boosting BC's rapidly developing telemedicine initiatives. Unlike other North American surgical centres that incorporate anatomy or animal laboratories, the Vancouver centre will link, technologically, to these facilities at the UBC medical school and other sites. A “smart classroom” will be connected to the hospital's trauma unit, emergency department and operating rooms. “I can communicate with my students in the whole province,” he says. “Our emphasis is on high tech to facilitate our educational goals.” These goals include the new problem-based curriculum, which depends on small-group tutoring that requires far more professors than the university can afford. Thus, the high-tech solution. Qayumi also believes the centre will raise the quality of the physicians UBC is producing. “We hope the centre will provide the facilities so that once we teach our students and residents something, they will have a consistent base to come to and practise more and more to sharpen their skills.” With help from his son Tarique, Qayumi has also developed interactive “Cyberpatient” software that allows students to take the history of a virtual patient and carry out an examination and offer treatment, while receiving voice and physical responses, such as facial expressions. The software is being tested in 15 medical schools, and results will be available by the end of 2002. “Nobody has tested the validity of these kinds of programs,” says Qayumi. “We want to compare computer-assisted learning with traditional textbook learning.” Eventually, says Qayumi, technology will supplant animals and “human guinea pigs” in the training of surgeons. For example, pressure-based technology currently under development at BC's Simon Fraser University will let students manipulate a surgical instrument and “feel” tissue, and in the process learn “how much to pull, how much to push, how much to hold things together.” However, he insists that the technology is not supposed to replace live patients but to provide “a better learning environment before they go and touch a patient.” He thinks the software will be useful as early as the second year of undergraduate training. This is all a far cry from the conditions at his beleaguered alma mater, the University of Kabul. Qayumi admits that he “doesn't know how he can be useful” during Afghanistan's rebuilding process. “If somebody can convince me that there is money and resources and people to support me so that I can go and build something, and tell me that I am the right person, I'll go,” he says. “I'll go tomorrow.” — Heather Kent, Vancouver
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,015 | 0,009 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,006 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,006 |
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 ».