A method for the madness: An international survey of health professions education authors’ journal choice
Bibliographic record
Abstract
INTRODUCTION: Scholarship is a key activity in health professions education (HPE). When disseminating scholarly work, how one selects the journal to which they submit is often argued to be a key determinant of subsequent success. To draw more evidence-based recommendations in this regard, we surveyed successful scholars working in HPE regarding their perspectives and experiences with journal selection. METHODS: We conducted an international survey of HPE scholars, investigating their decisions regarding journal choice. Corresponding authors were identified from a sample of 4000 papers published in 2019 and 2020. They were invited via email with up to four reminders. We describe their experience and use principle component and regression analyses to identify factors associated with successful acceptance. RESULTS: In total, 863 responses were received (24.7% response rate), 691 of which were included in our analyses. Two thirds of respondents had their manuscripts accepted at their first-choice journal with revisions required in 98% of cases. We identified six priority factors when choosing journals. In descending order of importance, they were: fit, impact, editorial reputation, speed of dissemination, breadth of dissemination, and guidance from others. Authors who prioritised fit higher and who selected a journal earlier were more likely to have their manuscripts accepted at their first-choice journal. DISCUSSION: Based on our results we make three recommendations for authors when writing manuscripts: do not be disheartened by a revise decision, consider journal choice early in the research process, and use the fit between your manuscript and the journal as the main factor driving journal choice.
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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 | 0.041 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".