Publication of Abstracts Presented at an International Healthcare Simulation Conference
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine the publication rate for abstracts presented at the International Meeting for Simulation in Healthcare (IMSH) and the time between abstract presentation and publication. We also aimed to describe the study features influencing subsequent publication and the relationship between these features and journal impact factors (IFs). METHODS: All types of accepted abstracts from the 2012 and 2013 IMSH were reviewed. We extracted the following data from each abstract in duplicate: presentation format, subject, type of scholarship, research method, study design, outcome measure, number of institutions in authorship group, and number of study sites. PubMed and Google Scholar were searched (January 1, 2012 to August 1, 2016) using the names of the first, second, and last author for comparison with abstracts. Journal of publication and IF were recorded. Data were summarized with descriptive statistics. Bivariate and multivariate analysis was performed to explore the association between publication status and other variables. RESULTS: Of 541 abstracts, 22% (119/541) were published with a median time to publication of 16 months (interquartile range = 8.525), ranging from 0 to 43 months. The study characteristics associated with a greater likelihood of publication were the following: research-type abstract, quantitative studies, randomized trials, studies with patient or healthcare-related outcomes, multiple institutions represented in authorship group, and multicenter studies. Studies with multiple institutions in authorship group and multicenter studies were published in higher IF journals (P < 0.05). CONCLUSIONS: The publication rate of 22% for abstracts presented at IMSH is low, indicative of the relatively new nature of simulation-based research in healthcare.
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 | 0.076 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".