Bibliographic record
Abstract
You have brains in your head. You have feet in your shoes. You can steer yourself any direction you choose.’ Dr Seuss1 With such an inspirational message, is it any wonder that Oh, the Places You'll Go!, Dr Seuss’ final publication, became one of the best-selling children's books of all time?2 Although I am not one to argue with genius, if I were to be totally honest, I would have to admit that my views on the sentiment expressed are self-contradictory. It's a message that I believe worth promoting even while I consider its content to be a flight of fantasy. Readers of Medical Education work in a field full of individuals who have considerable ambitions and more than enough skill to direct themselves down a fulfilling career path of their own choice. Yet what happens when you ask people in health professional education (HPE) or HPE research to describe how they came to their current position? If your experience is anything like mine, you will commonly hear some version of ‘dumb luck’, ‘pure chance’ or ‘I needed a job’. Sometimes the catalyst is a fortunate relationship with an influential role model; at other times it's an unexpected challenge or experience that changes the way we see the world or our own experience in it. Hardly ever do respondents suggest they truly steered themselves in the direction they chose. Such responses remind us that a great deal of coincidence is mixed into each of the critical moments in one's life and steers most of us to places that we couldn't anticipate, let alone choose. I personally identify most with Norman's description of the second generation of health professional academics3 in that I did choose to pursue a career in the field (albeit very late in my undergraduate training). Still, I can easily point to a handful of personal experiences that made my process of coming to that decision feel like a random walk through fields of serendipity and forests of chance. Those experiences were often tied to opportunities to learn from the many who had ploughed those fields before me. As I've written elsewhere, I believe it's these moments that offer some of the best fortune enjoyed by those of us who have followed the HPE pioneers.4 To celebrate three of those success stories we asked David Irby,5 Richard Reznick6 and Cees van der Vleuten7 to reflect on what they have learned over the courses of their remarkable careers and on how their perspectives in the field of health professional education have evolved. These luminaries are the second set of three individuals to win the Karolinska Institute Prize for Research in Medical Education. Their articles follow a similar set of reflections authored in 2011 by the first series of winners.8 On reading their submissions, I was struck by the extent to which even these giants, all of whom are incredibly talented and in all likelihood would have been successful in any domain they chose, pointed to situational factors that were critically influential in shaping their career paths. Further, I was encouraged by the consistency with which their articles indicated careers that were marked by lessons learned along the way rather than by a capacity to predetermine success, an ability to control what would happen, or the expectation that every experience would be one that led smoothly down a linear trail. To be fair, Dr Seuss’ tale does make it clear that not everything will go as expected, so my disagreement with the sentiment that opened this editorial is not as fervent as it might seem. In fact, the winding path is an excellent metaphor for the spirit we strive to emphasise in Medical Education: we try to encourage remaining open to new understanding and the sharing of genuine lessons learned through one's research and innovation efforts, rather than encouraging an attitude that treats the scientific enterprise as a mechanism for proving one's perspective.9 In the spirit of chance favouring the prepared mind (as suggested by Pasteur), we offer these reflective pieces from the Karolinska winners to promote reflection in our readers about the unexpected moments and opportunities they have encountered in order to stimulate them to think about how they might productively disrupt the perspectives and career paths of others. I would also like to use this editorial to recognise and celebrate a few additional individuals who continue to use whatever forces move them to help take our field from strength to strength. At the Association for the Study of Medical Education meeting which took place in Brighton, UK, within days of this printing, the 2014 Medical Education award winners were announced. We are extremely grateful for the contributions of these individuals and for the continued excellence demonstrated by all of our authors and reviewers. The Silver Quill Award, given to the article from the preceding year that was downloaded at the highest rate, goes to Dr Arthur Frank (University of Calgary, Canada), for his paper entitled ‘From sick role to practices of health and illness’.10 The Henry Walton Prize, given to the Really Good Stuff paper from the preceding year downloaded at the highest rate, is awarded to Dr Neil Aggarwal and Dr Ravi DeSilva (Columbia University, USA), for their article entitled ‘Developing cultural competence in health care professionals: a fresh approach’.11 The Choice Critics Awards, for outstanding contributions to Medical Education as peer reviewers, are given to Dr Jane Conway (Newcastle University, Australia), Dr Mathieu Nendaz (University of Geneva, Switzerland), Dr Debra Klamen (University of Southern Illinois, USA), Dr Diana Dolmans (Maastricht University, the Netherlands), Dr Samy Azer (King Saud University, Saudi Arabia), Dr Maria Blanco (Tufts University, USA), Dr Karen Mann (Dalhousie University, Canada) and Dr Heather Alexander (Griffith University, Australia). The Medical Education Travelling Fellowship is awarded to Dr Zareen Zaidi (University of Florida, USA) in support of her travel to Maastricht University to pursue collaboration around applying critical discourse analysis to study cultural influences in educational interactions. And, finally, the 2014–2015 Medical Education editorial interns12 will be Dr Gabrielle Finn (University of York, UK), Dr Ilhim Youssry (Cairo University, Egypt) and Dr Lara Varpio (Uniformed Services University of the Health Sciences, USA).
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 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.067 | 0.025 |
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".