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Enregistrement W2605850064 · doi:10.5489/cuaj.4503

From cue cards to code rot: In the end, it's not how you acquire the content, it’s what you do with it

2017· editorial· en· W2605850064 sur OpenAlexaffvenue
Andrew E. MacNeily

Notice bibliographique

RevueCanadian Urological Association Journal · 2017
Typeeditorial
Langueen
DomaineEngineering
ThématiqueBiomedical and Engineering Education
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésContent (measure theory)Code (set theory)End-to-end principleComputer scienceLinguisticsArtificial intelligenceProgramming languageMathematicsPhilosophy

Résumé

récupéré en direct d'OpenAlex

Cue cards, remember those?Mine were alphabetized, 5" x 8" cue cards.They were packed with lists, diagrams, and classifications, neatly squeezed into a large folio box.That's what I used for studying right up to the moment before my Royal College examinations in 1991.When I first started practice, I kept them in the office as a sort of security blanket in case I needed to access them for an urgent refresher.I still have them, except now they are at home on the top shelf of a closet next to a dusty box full of 8 mm Kodachrome II film reels containing the secrets of my childhood.I sometimes wonder if those cue cards would still be in my office if it weren't for the internet and smart phones.Part of the process of becoming a urologist is to become a content expert; in order to do that, trainees must access the content, learn it, and reproduce it on a standardized test.Over the years, I have had the privilege of contributing to the education of over 60 residents.During this time, I have observed the evolution of their study habits from cue cards and long, hand-written study notes to PowerPoint presentations, YouTube videos, social media, and digital files.Each has helped to parse urology into ever-smaller bits for the human brain to recognize and retrieve.In this issue of CUAJ, Skinner et al from Queen's University have provided us with an interesting descriptive study of two years of Canadian graduates, how they study, how much they study, and what motivates them to study throughout their residency. 1Not surprisingly, the reported time spent studying increased progressively throughout the years of training.The overwhelming motivator for studying in the final year was the Royal College certifying examination.It should be sobering to faculty that didactic lectures from us were rated as a rather mediocre method of content acquisition.As alluded to in the study, one of the central pillars supporting the shift from a timebased model to a competence by design (CBD) model of medical education is the desire to move away from content and towards competence.It has been recognized that being a content expert is necessary, but insufficient for the delivery of safe and appropriate care.In other words, when you lose control of the renal vein, the books are closed, the cue cards are in their box, your smart phone is inaccessible, and nobody is going to tweet you out of this situation.The unstated challenge in this paper then relates to how we teach and evaluate competence -not just the technical components of surgical competence, but the cognitive ones as well.How do we teach and assess communication, collaboration, and decision-making in the time-crunched ambulatory and operative setting?Decisionmaking -MD also stands for making decisions and making them under pressure based on incomplete information, and then taking responsibility for those decisions.No amount of content expertise, code rot or not, can replace that ability.How can we better teach and assess this?In addition, if the examinations are moved to the penultimate year of training, as is being proposed in the new CBD framework, what will replace the exam (if anything) to motivate trainees to study during the transition to practice phase of training?The members of the Urology Specialty Committee from across Canada have been hard at work trying to answer questions like these.Over the last 15 months, we have volunteered a total of nine days over three visits to the Royal College and an additional eight hours of teleconference time arm-wrestling our way toward some form of consensus on CBD implementation for urology.We are not there yet, and the result won't be perfect, but we are getting closer to accommodating the many institutional and regional differences of opinion.The go-live date is looking like July 1, 2018.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,027
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,027
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0040,002
Études des sciences et des technologies0,0040,003
Communication savante0,0080,005
Science ouverte0,0030,002
Intégrité de la recherche0,0130,021
Charge utile insuffisante (le modèle a refusé de juger)0,0220,017

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,221
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission2
Résumé présentoui

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