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Enregistrement W1875463914 · doi:10.1111/j.1365-2923.2010.03821.x

Getting out there: developing an abstract editing circle

2010· article· en· W1875463914 sur OpenAlexaffabout
Lara Varpio, Mish Boutet, Meridith B. Marks

Notice bibliographique

RevueMedical Education · 2010
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueArtistic and Creative Research
Établissements canadiensUniversity of OttawaMedical Council of Canada
Organismes subventionnairesnon disponible
Mots-clésMentorshipSession (web analytics)Context (archaeology)AppealScholarshipMedical educationPsychologyPublic relationsMedicineComputer sciencePolitical scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Context and setting As the medical education field develops, more researchers seek to disseminate their scholarship at conferences. Although the number of submissions to conferences is growing, the amount of podium time has remained relatively static. Consequently, abstract evaluation processes are becoming increasingly competitive. Why the idea was necessary In 2007, recognising the need to help local medical education researchers compete for podium time at high-impact conferences, we developed an abstract editing circle (AEC). The purpose of the AEC is to help researchers write conference abstracts that more effectively present their research findings with the aim of improving their chances of acceptance. Our AEC provides two kinds of support. Firstly, AEC participants receive writing mentorship from senior medical education researchers. Secondly, the AEC fosters the development of peer groups that can provide ongoing writing support. What was done The AEC was advertised to local medical educators as a means to prepare abstract submissions for national and international conferences. Participation in the AEC was limited to nine people. Participants were placed into working groups of three people. Each group was assigned a local mentor, recruited by the AEC’s coordinator. The AEC is constructed from three elements. Firstly, three monthly instructional meetings were held with AEC participants. Each hour-long meeting was led by a different noted medical education researcher. Each session addressed specific abstract writing techniques and insights from the speaker’s experiences on conference review committees. Topics included: discourse analysis of previously accepted abstracts; strategies for presenting findings that will appeal to large audiences, and writing effective titles. The second element concerns feedback from local peers and mentors. Participants attended the first meeting with initial drafts of their abstracts. Following this meeting, participants: (i) exchanged abstracts with their working group peers and mentor; (ii) edited one another’s abstracts according to insights gained from the meeting, and (iii) reworked their own abstracts based on feedback from peers and mentor. Participants arrived at the second meeting with revised abstracts. At the second meeting, participants were grouped into new working groups with new mentors. After the second meeting, the abstract exchange, editing and reworking processes were repeated within the new groups. This structure was repeated again after the third meeting. The third element refers to editorial review from senior Canadian medical education scholars. After the last group review, all participant abstracts were e-mailed to three senior mentors (recruited nationally). These mentors reviewed and commented on the abstracts and then returned them to the participants. Participants finalised their abstracts and submitted them to conference competitions. By the time it came to be submitted, each participant’s abstract had been potentially reviewed by eight peers and six senior mentors. Evaluation of results and impact Seven abstracts from the 2007–2008 cohort of nine participants and four from the six participants in the 2008–2009 cohort were accepted for national or international conferences. The primary obstacle for the AEC has been organising meetings to fit in with the schedules of participants, mentors and presenters. Demand continues locally and modified versions of the AEC have since been adopted at three other Canadian universities. We are running the AEC in our local community again this year.

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)0,114
score de la tête « metaresearch » (Gemma)0,288
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Présentation des résultats · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,886
Score d'incertitude au seuil0,604

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1140,288
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,002
Études des sciences et des technologies0,0120,006
Communication savante0,0180,016
Science ouverte0,0060,022
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0130,012

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 Opus0,044
Tête enseignante GPT0,365
Écart entre enseignants0,321 · 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.

Devis d'étudeQualitatif
DomainePrésentation des résultats
GenreEmpirique

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

En bref

Citations2
Publié2010
Routes d'admission2
Résumé présentoui

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