Ranked list of training needs
Bibliographic record
Abstract
Background: Scientific editors (i.e., those who make decisions on the content and policies of a journal) have a central role in the editorial process at biomedical journals. However, very little is known about the training needs of these editors or what competencies are required to perform effectively in this role. Methods: We conducted a survey of perceptions and training needs among scientific editors from major editorial organizations around the world, followed by a modified Delphi process in which we invited the same scientific editors to rate the importance of competency-related statements obtained from a previous scoping review. Results: A total of 148 participants completed the survey of perceptions and training needs. At least 80% of participants agreed on six of the 38 skill and expertise-related statements presented to them as being important or very important to their role as scientific editors. At least 80% agreed on three of the 38 statements as necessary skills they perceived themselves as possessing (well or very well). The top five items on participants’ list of top training needs were training in statistics, research methods, publication ethics, recruiting and dealing with peer reviewers, and indexing of journals. The three rounds of the Delphi were completed by 83, 83, and 73 participants, respectively, which ultimately produced a list of 23 “highly rated” competency-related statements and another 86 “included” items. Conclusion: Both the survey and the modified Delphi process will be critical for understanding knowledge and training gaps among scientific editors when designing curriculum around core competencies in the future.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.045 |
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".