Learning best-practices in journalology: course description and attendee insights into the inaugural EQUATOR Canada Publication School
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Dissemination of research results is a key component of the research continuum and is commonly achieved through publication in peer-reviewed academic journals. However, issues of poor quality reporting in the research literature are well documented. A lack of formal training in journalology (i.e., publication science) may contribute to this problem. To help address this gap in training, the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Canada Publication School was developed and facilitated by internationally-renowned faculty to train researchers and clinicians in reporting and publication best practices. This article describes the structure of the inaugural course and provides an overview of attendee evaluations and perspectives. KEY HIGHLIGHTS: Attendees perceived the content of this two-day intensive course as highly informative. They noted that the course helped them learn skills that were relevant to academic publishing (e.g., using reporting guidelines in all phases of the research process; using scholarly metrics beyond the journal impact factor; open-access publication models; and engaging patients in the research process). The course provided an opportunity for researchers to share their challenges faced during the publication process and to learn skills for improving reproducibility, completeness, transparency, and dissemination of research results. There was some suggestion that this type of course should be offered and integrated into formal training and course curricula. IMPLICATIONS: In light of the importance of academic publishing in the scientific process, there is a need to train and prepare researchers with skills in Journalology. The EQUATOR Canada Publication School provides an example of a successful program that addressed the needs of researchers across career trajectories and provided them with resources to be successful in the publication process. This approach can be used, modified, and/or adapted by curriculum developers interested in designing similar programs, and could be incorporated into academic and clinical research training programs.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".