UOJM editor training: results from the 2013 editor satisfaction survey and highlights from 2013-2014 training workshops
Bibliographic record
Abstract
BACKGROUND UOJM recognizes that editor competency and preparedness directly impacts the quality of peer review, which holds the key to producing a great publication. We believe that success of our journal is based on a central goal of promoting physician competency in medical communication and developing leaders in medicine. In the age of evidence-based medicine, there are surprisingly few, if at all, opportunities for medical trainees to gain formal training in scientific writing and critical appraisal. Over the last two years, the UOJM has aimed to address these gaps and worked on developing a training program to equip participants with these important skills. Indeed, the merits of involvement in a peer reviewed journal at this stage of medical/research training have been recognized by its trainee participants, and have been reviewed extensively by Kevin Lee [1]. Following the success of the 2013 issue, UOJM made considerable strides to further improve the quality of content in the journal. In 2012-2013, 30 students participated as reviewers on the editorial board and received a practical experience in peer reviewed research. We conducted a year-end survey to identify issues and areas for improving the editor experience.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".