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Enregistrement W2048013174 · doi:10.1111/tct.12361

Tools, insights and feedback

2015· editorial· en· W2048013174 sur OpenAlexaboutno aff
Jill Thistlethwaite

Notice bibliographique

RevueThe Clinical Teacher · 2015
Typeeditorial
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScholarshipWorkforceHealth careVariety (cybernetics)Quality (philosophy)Medical educationPublic relationsPsychologyMedicinePolitical scienceComputer scienceLaw

Résumé

récupéré en direct d'OpenAlex

Another year, another volume of The Clinical Teacher, and some new types of articles. Because of the lead time for journals, I am writing this in Minneapolis in November, where I will be staying for 4 months at the National Center for Interprofessional Practice and Education. There is a lot going on: health care reform; the Ebola crisis (with three cases so far in the USA, and difficult to predict what will happen, although many more cases are expected in West Africa); escalating medical costs; disagreements about workforce numbers (and how much of the problem of access to health care is related to how many workers there are and how much is related to poor distribution); and concerns about the quantity and quality of clinical placements across all the professions. These problems are similar to those in other parts of the world, and members of the health care professions need to ensure that society has the most up-to-date information; they should work together as colleagues and always be exemplary role models for the next generations. In this edition of the journal we introduce the new Insights articles. These are reflective pieces on important topics in clinical education, based in good scholarship and with references to relevant evidence. Since we called for Insights last year, we have received many submissions on a huge variety of topics and from all grades of health professionals. The quality has been variable and what has often been lacking is the reflection and the message for the readers: busy clinical teachers. Like all good writing Insights should grab readers from the first sentence, drawing them into the story. Amit Parekh does this at the start of the article on X-ray teaching in Africa. We can all relate to: ‘It was the hardest teaching session I have ever delivered’.1 We want to know why it was hard, how the teacher overcame the difficulty, and what was learned in the process. The authors from Mexico tell a tale of how to use resources that come to hand as teaching tools when there is a limited budget.2 This paper references one of the founders of the Mayo Clinic and Benjamin Franklin, demonstrating that writers also need to be well read to engage their readers. We also unveil the Clinical Teacher’s Toolbox papers: articles focusing on tools for clinical teachers with the aim of enhancing learning, teaching and writing. We hope they are practical and engaging. The first is a new look at feedback. This topic is covered regularly in health professional education journals, and continues to challenge and confuse learners and teachers. In evaluation surveys, students frequently cite the provision of feedback as one of the least well done features of their courses. Many reasons have been suggested for this, including that students don't understand what feedback is, that they are rarely observed interacting with patients so any feedback is not specific or timely, that feedback really is done badly. In their original article in this issue, Pincavage and Cifu note that it is difficult to obtain sufficient written feedback for and about students on clinical rotations from their supervisors.3 They looked at the outcome of giving feedback about feedback, to improve its quality, and found that there was improvement amongst the lower performing faculty members. In the ‘toolbox’ article by David Boud, he discusses how feedback may and should contribute to the continuing learning of students, and the need to ensure that it actually does bring about change through a feedback loop.4 Boud has been involved in research and teaching development in higher and professional education for over three decades, and has written widely on this topic. For those of us brought up on Pendleton's guidelines and Ende's process,5, 6 methods mainly used for delivering feedback at a given moment in time, Boud's approach may be challenging for time-poor clinical teachers; however, the article should certainly stimulate discussion and we hope you will find it useful. The Insights paper on course evaluation also features feedback: the feedback from learners to their teachers.7 Again, this type of feedback should be a dialogue, and learners need to know that their feedback had led to change in future iterations of the course or programme. Response rates for evaluation surveys are notoriously low if participants do not feel that their feedback is likely to be used in any way to shape change. Although not specifically a toolbox paper, the article by one of our newer associate editors looks at a framework for helping educators develop, design and manage simulation.8 Hossein Khalili is an academic nurse based in Ontario, whose recently awarded PhD focused on interprofessional socialisation, and he is passionate about the importance of simulation in the development of collaborative practice skills across the health professions. The mention of collaborative practice brings me back to the start of this editorial. One of the most rewarding aspects of my clinical and academic role is the opportunity to meet and work with a wide range of people from diverse professions and disciplines, from many countries, and with varying outlooks on education and health care. Working in a team (such as with The Clinical Teacher) and with wider collaborations (such as across institutions and nations) continually challenges my beliefs, biases and ways of working. We never stop learning; we should never stop seeking out feedback and acting on it. We welcome your feedback on this edition of the journal, and aim to act upon it so that the journal goes from strength to strength.

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)0,044
score de la tête « metaresearch » (Gemma)0,221
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: aucune
Score de désaccord entre enseignants0,134
Score d'incertitude au seuil0,449

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0440,221
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,006
Études des sciences et des technologies0,0040,004
Communication savante0,0230,016
Science ouverte0,0030,012
Intégrité de la recherche0,0040,007
Charge utile insuffisante (le modèle a refusé de juger)0,1340,100

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 Opus0,108
Tête enseignante GPT0,449
Écart entre enseignants0,341 · 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

Citations24
Publié2015
Routes d'admission1
Résumé présentoui

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