MP26: The simulated newsroom: a novel educational intervention to teach advocacy skills to resident physicians
Bibliographic record
Abstract
Innovation Concept: Advocacy is a key competency of Canadian residency education, yet physicians seldom engage with supra-clinical advocacy efforts upon completion of training. Emergency medicine (EM) residency training may not equip graduates with the knowledge and skills required to engage as physician-advocates in their communities. Focused writing workshops may increase the confidence and ability of EM trainees to engage as health advocates. Methods: Following a literature review, simulated newsroom workshops were developed by two EM physicians with graduate-level journalism training and workplace experience. Participants were invited to participate in an audio-recorded focus-group and to submit their opinion editorial. Twelve participants registered for the workshops and six attended both sessions and the focus group; four submitted written work. Focus group transcripts and written work were qualitatively analysed to understand acceptability, feasibility, and how students might engage as future health advocates. Curriculum, Tool, or Material: The simulated newsroom consisted of participants acting as journalists and the expert facilitator acting as a news editor. The first workshop provided a framework for news judgement in a didactic session, followed by interactive exercises including: prioritization of news pitches, a simulated editorial meeting, and analysis of published news articles. The participants then drafted their own pitches for in situ feedback from peers and facilitators. Two-weeks later, participants brought their completed articles for peer and expert review before submitting their final article. Conclusion: The innovation bolstered resident physician confidence in advocacy through the popular press, and provided demonstrable skills in opinion writing. Participants felt challenged to develop compelling narratives and differentiate this form of advocacy communication from academic writing or prior media training. Participants valued the workshop as a voluntary component of residency education led by peer experts. Through their writing, residents demonstrated an understanding of structural factors that impact patient health and health systems. Future engagement as physician advocates may be tempered by fears of professional repercussions for public engagement; the impact of physician advocacy on population health outcomes is not yet known.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".