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Getting out there: developing an abstract editing circle

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

Bibliographic record

VenueMedical Education · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsUniversity of OttawaMedical Council of Canada
Fundersnot available
KeywordsMentorshipSession (web analytics)Context (archaeology)AppealScholarshipMedical educationPsychologyPublic relationsMedicineComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.114
metaresearch head score (Gemma)0.288
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.604

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.288
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.002
Science and technology studies0.0120.006
Scholarly communication0.0180.016
Open science0.0060.022
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0130.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.365
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainReporting
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2010
Admission routes2
Has abstractyes

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