Bibliographic record
Abstract
Background: Measuring journal club fidelity (JCF) is essential in interpreting the impact of this intervention on targeted outcomes. To our knowledge, there are no studies that demonstrate how JCF can be measured and linked to educational outcomes. Furthermore, there is no standard JCF measure, nor are there any studies on implementing such a measure. Purpose: To describe the design of, and implementation process for, JCF evaluation rubrics. Method: A descriptive study of the steps involved in measuring JCF, stemming from a quasi-experimental study and relying on the self-assessment rubrics completed by nursing students (n = 48) during the fourth and seventh sessions, the evaluation rubrics filled in by two observers during the second, third, sixth, and seventh sessions, and the video used during the third and seventh sessions. The study also included a comparison group (n = 50). Results: The structure of the journal club (JC) was adhered to. Given the context, however, some criteria related to the JC process were not fully consistent with pre-established standards: the submission of research papers, the vicarious experience, regular verbal feedback, circumstances conducive to stress management, empowerment strategies, and active participation by students. Conclusion: The results support the importance of making JCF criteria explicit, evaluating these criteria and using them to interpret the results, and identifying potential moderating factors.
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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 | 0.045 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".