P018: How to get your departmental web content to work for you: one department's experience with free open access medical education
Bibliographic record
Abstract
Innovation Concept: Free open access medical education (FOAM) is a quickly growing field. While there is an abundance of resources online, and on social media, the quality of those resources should always be questioned and reviewed. Furthermore, as medical learners progress in their training, they become lead consumers and producers of FOAM. Our educational innovation concept was the introduction of two FOAM streams into our residency program to assist learners to produce their own content with mentorship from our emergency medicine faculty. Methods: Medical students and residents training in the emergency department were encouraged to submit content to either our department website in the form of a clinical PEARL, or a research paper to the departmental Cureus online journal. All website content was reviewed by an attending physician and all Cureus content was submitted for further peer review and publication if approved. All published content was shared on social media through our department's Twitter account. A select number of residents were also mentored in reviewing and editing FOAM content and publishing it to our departmental website. Curriculum, Tool or Material: sjrhem.ca is the Saint John Regional Hospital Department of Emergency Medicine's website. A portion of the website is dedicated to posts arising from departmental rounds, case reviews as well as posts from learners in the form of clinical PEARLS. They are designed as succinct and informative clinical summaries and allow learners to share their content to a wider audience online. Cureus.com is an online journal of medical science, with a dedicated Dalhousie Emergency Medicine Channel. The editors are local emergency medicine faculty and senior residents, while reviewers are independent. In the last year, the clinical pearls received 5672 views, and the Cureus channel received 1143 content views. Conclusion: Feedback from learners regarding publication of their own FOAM has been positive and has allowed them to share their content to a much wider audience through our Departmental Website, Cureus Channel and Twitter stream. Furthermore, we are helping to prepare residents to produce their own high quality content, allowing our FOAM program to grow.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".