Break out the champagne and caviar: a toast to <i>Medical Education's</i> award winners
Bibliographic record
Abstract
Very recently, at the Annual Scientific Meeting of the Association for the Study of Medical Education (ASME) in Glasgow, we celebrated a sample of those who made strong contributions to the successes of Medical Education during the past year. In thinking about how to present them in this editorial (along with a few additional announcements), I found inspiration in the words of the authors who enliven our pages. That is, I've scoured through the list of article titles published in 2018 to identify a few fitting phrases. They are taken completely out of context, such that you should infer nothing about the article from the way in which I've used the selected text. I do hope, however, that the intrigue created by these turns of phrase encourages you (as it did me) to go back and enjoy the poetry many of our authors embedded in the impactful prose they produced. It goes without saying that the editorial team faces a substantial challenge curating the set of articles we hope our readers will find most useful from a much larger volume of submissions. My best estimate is that we have processed more new submissions in the 11 years leading to the end of 2018 than the journal received in its preceding 41-year history. 2018 witnessed the addition of 1687 new submissions from 82 countries to that mix. To treat those submissions as fairly as possible, we drew upon the perspectives of 934 reviewers who volunteered their service from around the globe. In support of our ongoing effort to use the peer review process as a constructive one that has benefit to authors regardless of the decision made on any particular paper, I am pleased to report that Wiley has generated a Peer Review Resource Centre to assist reviewers with their charge. Now proudly made accessible through mededuc.com (see ‘CME for Reviewers’), reviewers and would-be reviewers are encouraged to take advantage of this resource to learn how to ensure that the time and expertise spent to construct a peer review is optimally directed at facilitating the development of the work being reviewed. Similarly competitive is the adjudication process for the Medical Education Travelling Fellowship. ASME and Wiley have again generously identified funding to facilitate the development of experience, training and knowledge that will enable strengthening of conceptually grounded, methodologically rigorous and programmatic research efforts. This year's winner is Anique Atherley (Western Sydney University, Australia and Maastricht University, the Netherlands), who will be striving to develop expertise in the use of social network analysis to study how relationships influence students’ transition to clinical training. Among the other ‘public judgements’ that were celebrated in Glasgow are the awards for those articles that have most enticed our readership. In the past 5 years, Medical Education articles have been downloaded over 3.3 million times. We mark those that stand out each year through presentation of the Silver Quill Award (granted to the authors of the article from the preceding year that was downloaded most often) and the Henry Walton Prize (so named in honour of Medical Education's longest serving Editor in Chief). For 2018, the former was won by Hessler, Pöpping, Hollstein, Ohlenburg, Arnemann, Massoth, Seidel, Zarbock and Wenk (Germany) for their work entitled ‘Availability of cookies during an academic course session affects evaluation of teaching’.1 The latter parallel award for Really Good Stuff articles was won by Archer and Meyer (South Africa) for their work ‘Teaching empathy to undergraduate medical students: “One glove does not fit all”’.2 In many ways, this is the item that should have kicked off any discussion of the past year, given that a dominant focus for the journal has been the transition of our editorial office from its long-standing home in Plymouth into the Wiley environs in Oxford.3 Those of you who have submitted or reviewed articles in the past year will have been introduced already to Iris Poessé and Anna Rivers, but I mention their names here to thank them for their considerable and impressive efforts at keeping this ship moving forward. Not only have they been able to continue our day-to-day operations with an enviable smoothness, but both have shown themselves eager to offer and take on new initiatives to help the journal move from strength to strength. Many readers will already have noticed that we are in the midst of cleaning up our web presence in the Wiley On-line Library (mededuc.com). You will also notice a substantial format change for the pages of the journal itself in the near future. If you are pleased with any of the interactions you have had with those working on Medical Education, I would encourage you to take a moment to thank them for their efforts and their leadership.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".