Utilizing a Journal Club to Build Research Competencies in a Cross-Disciplinary Environment
Bibliographic record
Abstract
Background The goal of this process-focused field note is to detail the steps taken to design and implement the pilot of a cross-disciplinary research-trainee-led journal club within a hospital-based research institute.Analysis The journal club was designed to support learning goals in the areas of critical appraisal, research knowledge, and communication within a cross-disciplinary environment. The evaluation data for the three pilot sessions are presented, and successes and challenges are discussed.Conclusion and implications Recommendations for institutes interested in the implementation of similar programs focused on research competencies within a cross-disciplinary setting are given. Keywords Knowledge dissemination; Tools and practices; Journal club; Research training; Interdisciplinary communication; Program development Résumé Le but de cette note de terrain est de détailler les étapes de la conception et exécution du pilote d’un club de lecture scientifique interdisciplinaire dirigé par des étudiants au sein d’un institut de recherche en milieu hospitalier. Le club de lecture a été conçu pour soutenir des objectifs d’apprentissage relevant des domaines de l’analyse critique, du savoir, et de la communication de recherche au sein d’un environnement interdisciplinaire. Les données d’évaluation pour les trois sessions pilotes sont présentées dans cet article et les succès et défis discutés. Des recommandations sont faites pour les instituts intéressés par l’exécution de programmes similaires axés sur des compétences de recherche dans un cadre interdisciplinaire. Mots clés Knowledge dissemination: Tools and practices; Journal club; Research training; Interdisciplinary communication; Program development
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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 | 0.059 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".