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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".