Disseminating evidence in medical education: journal club as a virtual community of practice
Bibliographic record
Abstract
BACKGROUND: This study explores the impacts of the Council on Medical Student Education in Pediatrics (COMSEP) Journal Club, a unique means of providing monthly professional development for a large international community of pediatric undergraduate medical educators. In particular, we sought to establish member engagement with the Journal Club, identify factors impacting member contributions to the Journal Club, and determine perceived benefits of and barriers to participation as a Journal Club reviewer. METHODS: Using an established Annual Survey as a study instrument, six survey questions were distributed to members of COMSEP. Items were pilot tested prior to inclusion. Quantitative data were analyzed using descriptive statistics and chi-square analysis.. RESULTS: Of 125 respondents who completed the survey, 38% reported reading the Journal Club most months or always. Level of engagement varied. Reasons for reading included a topic of interest, keeping up to date on medical education literature, gaining practical tips for teaching and implementing new curricula. Motivators for writing a review included keeping up to date, contributing to a professional organization, and developing skill in analyzing medical education literature, with a minority citing reasons of enhancing their educational portfolio or academic promotion. The most commonly cited barriers were lack of time and lack of confidence or training in ability to analyze medical education literature. CONCLUSION: As a strategy to disseminate the latest evidence in medical education to its membership, the COMSEP Journal Club is effective. Its format is ideally suited for busy educators and may help in members' professional development and in the development of a community of practice.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| 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".